卡通角色版权保护关键问题研究
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摘要
信息化深刻地改变了人类的生产与生活方式，在这一发展历程中，知识型经济与信息技术相生相成，受益良多。卡通作为最具有代表性的一类知识型经济产业在信息化过程中迎来空前的发展良机，其生产、传播、消费中的每一个环节都深受信息技术的影响，获得了迅猛发展。然而作为以版权为核心盈利模式的产业，卡通在信息社会中也面临着更为复杂的版权保护问题。
卡通产业的版权问题主要在于卡通媒体在发布、传播、应用过程中未经授权的浏览、分享、仿制等行为，侵权行为呈现载体多样化、跨行业的特点。传统的基于数字签名或数字水印的版权保护技术已经难以应对针对卡通的复杂的侵权行为，产业界对面向卡通产业的版权保护技术的需求十分迫切。
卡通角色是卡通作品的关键内容和衍生价值最高的部分，也是侵权行为最主要的目标；无论是对卡通作品的直接盗用还是对卡通作品的仿制，卡通角色都是其中不变的主题。所以卡通版权保护的核心就是对卡通角色的保护。
本文以图像为对象研究卡通产业的版权问题，主要研究针对卡通角色的侵权行为的搜寻、过滤以及卡通媒体的机密性保护问题。首先探讨了卡通图像的基本性质，并指出形状是构成卡通图像的最基本语义内容，具有形状的相似性是卡通角色在不同性质的图像中内在联系。提出自然图像及卡通图像中线条的提取方法，来构成图像中的基本形状；随后提出了局部不变形状特征，以在位置、尺度、角度都未知的复杂图像中，对卡通角色的内容进行表述。进而展开对卡通角色的检索、检测以及选择加密研究，以解决卡通产业所面临的版权保护问题。
本文的主要研究工作和创新点在于：
(1)针对自然图像与卡通图像分别提出了检测其中边缘及线条的方法来提取图像中的形状信息。针对基于学习的边缘检测中存在的标记不准及噪声数据干扰的问题，提出了基于FSVR学习的边缘检测算法。该算法根据训练数据的可信度来确定训练数据的模糊权值，利用模糊权值来将低可信度数据对训练的影响降低，实验分析表明该算法可以获得较同类算法更佳的边缘检测效果。随后本文研究了卡通图像中线条的分类，分析了装饰线与边缘在一阶、二阶高斯导数滤波响应的不同特性，提出了新的针对卡通图像的线条提取算法，所提出算法较同类算法具有更佳的视觉效果与更清晰区分装饰线与边缘的能力。
(2)提出了一种对尺度、方向变化都鲁棒的局部形状特征，并将该特征应用于对包含卡通角色形象的卡通图像、自然图像的检索中。该特征以卡通图像中常见的角点为特征点，对特征点附近邻域的边缘/线条的空间分布进行建模，从而获得对局部形状进行具有一定区分性与紧致性的描述。随后采用特征匹配的方法获得查询图像与待检索图像之间相似度。局部形状特征跨越了自然图像与非自然图像之间外观差异的鸿沟，同时排除背景信息的干扰，实现对局部内容进行检索。
(3)针对卡通图像互联网分享过程中的滥用问题，提出了基于Hough voting的卡通角色检测方法，供媒体分享网站对用户上传图像或视频进行过滤。首先采用人工分割的模板来学习表达卡通角色的特征词典及基于Hough voting的目标表达。随后对模板和待检测图像特征进行匹配基础上，根据目标的表达，在Hough voting空间中计算某个位置存在该卡通角色的后验概率，从而获得该角色出现的位置及相应的可能性。算法在对卡通图像的检测中显示出了其准确性及效率。
(4)提出了基于选择加密的卡通媒体分级预览方案，以在计算代价较低的情况下保证数据的机密性，并提供分级浏览的功能。首先基于薄板样条函数对已经定位于窗口内部的卡通角色精确提取外轮廓；随后基于混沌系统构造级联的序列密码算法对目标区域进行加密。选择加密的方法对核心内容进行了加密，而保留背景区域供人预览，提供了更好的用户体验性，提高了卡通媒体可推广性。
Nowadays, information technology changes human being’s life a lot. In thisprogress, knowledge-based economy and information technology influence eachother, and make greate advances. Cartoon industry is a typical knowledge-basedindustry, and gains a good opportunity for further developing. All the aspects ofcartoon industry, including the producing, broadcasting and consuming, benefit fromthe information technology and make a great progress. However, the informationtechnology also brings more complex copyright problems at the same time, whichmake the cartoon copyright protection an urgent problem.
The key issues of the copyright infringement problem in cartoon mainly lie onunauthorized browsing, sharing and copy during the cartoon broadcasting, spreadingand the application. Traditional copyright protection techniques, such aswatermarking and cryptography, are not sufficient for such complex problems.Obviously, cartoon characters are the key content of the cartoon media, which isalso the mostly frequently infringed part. Most of the infringements involve thecartoon characters, so the characters are the invariant theme in the infringements.Therefore, cartoon character protection is the key of the cartoon media copyrightprotection.
This thesis focuses on the copyright protection of cartoon media. Firstly, thespecialty of the cartoon image is discussed, and a basic viewpoint is proposed: shapeis the essential element to present the cartoon character and the similarity in shape isthe internal connection between same characters in different images. Secondly, edgeand curve detection methods are proposed to extract the shape in either natural andcartoon images. Then, a new local invariant shape feature is proposed to describethe cartoon characters in the image, in which the location, angle and scale ofcharacter are unknown. Based on this feature, the research on the cartoon characterindexing, detection and selective encryption are done.
The main contribution and innovation of this dissertation are described asfollows:
Firstly, the learning based edge detection methods for natural images arediscussed and then a new edge detection method based on Fuzzy Weighed SupportVector Machine for Regression is proposed to alleviate the label impression problemand noise. A fuzzy membership is applied to re-weigh the training sample accordingto the distance to the human labels, so that the samples with different reliability aretreated differently to alleviate the label imprecision problem. Then a curveextraction method for cartoon images is proposed to extract the shape in the cartoon image. The two types of curve in the cartoon image are defined and extractedseparately based on their different response to the first and the second orderdirectional derivative. The proposed method is more efficient and produces betterresults.
Secondly, a scale and rotation invariant local feature is proposed to describe thelocal shape, and used in the cartoon character retrieval in both the natural image andthe cartoon image. The feature locates at the scale invariant corner and use the shapecontext to describe the shape near the corner, and a feature matching method is usedto compute the similarity between the query and the image in the database. The usedof shape feature fill the gap between the appearance differences between naturalimage and cartoon image, and reduce the disturbance from the background.
Thirdly, a Hough voting based cartoon character detection method is studied topreventing the unauthorized sharing in the website. The visual shape words areextracted in the pre-segmented cartoon characters and then a Hough voting schemeis used to present the character. And then a posterior probability conditioned on theexistence of visual words is estimated in the Hough voting space. The proposedmethod is effective as the experimental results show.
Fourthly, selective encryption method is investigated in order to protect thecartoon from unauthorized copy. The Thin Plain Spline-Robust Point Matchingmethod is employed to localize the cartoon character accurately, and then a streamcipher based on multi-chaotic systems is used to encrypt and decrypt the characterregion. The selective encryption method encrypts the key content of the cartoon andleaves the others as plain for the preview, which can offer better user experiencewhich make cartoon publish more easily.
卡通产业的版权问题主要在于卡通媒体在发布、传播、应用过程中未经授权的浏览、分享、仿制等行为，侵权行为呈现载体多样化、跨行业的特点。传统的基于数字签名或数字水印的版权保护技术已经难以应对针对卡通的复杂的侵权行为，产业界对面向卡通产业的版权保护技术的需求十分迫切。
卡通角色是卡通作品的关键内容和衍生价值最高的部分，也是侵权行为最主要的目标；无论是对卡通作品的直接盗用还是对卡通作品的仿制，卡通角色都是其中不变的主题。所以卡通版权保护的核心就是对卡通角色的保护。
本文以图像为对象研究卡通产业的版权问题，主要研究针对卡通角色的侵权行为的搜寻、过滤以及卡通媒体的机密性保护问题。首先探讨了卡通图像的基本性质，并指出形状是构成卡通图像的最基本语义内容，具有形状的相似性是卡通角色在不同性质的图像中内在联系。提出自然图像及卡通图像中线条的提取方法，来构成图像中的基本形状；随后提出了局部不变形状特征，以在位置、尺度、角度都未知的复杂图像中，对卡通角色的内容进行表述。进而展开对卡通角色的检索、检测以及选择加密研究，以解决卡通产业所面临的版权保护问题。
本文的主要研究工作和创新点在于：
(1)针对自然图像与卡通图像分别提出了检测其中边缘及线条的方法来提取图像中的形状信息。针对基于学习的边缘检测中存在的标记不准及噪声数据干扰的问题，提出了基于FSVR学习的边缘检测算法。该算法根据训练数据的可信度来确定训练数据的模糊权值，利用模糊权值来将低可信度数据对训练的影响降低，实验分析表明该算法可以获得较同类算法更佳的边缘检测效果。随后本文研究了卡通图像中线条的分类，分析了装饰线与边缘在一阶、二阶高斯导数滤波响应的不同特性，提出了新的针对卡通图像的线条提取算法，所提出算法较同类算法具有更佳的视觉效果与更清晰区分装饰线与边缘的能力。
(2)提出了一种对尺度、方向变化都鲁棒的局部形状特征，并将该特征应用于对包含卡通角色形象的卡通图像、自然图像的检索中。该特征以卡通图像中常见的角点为特征点，对特征点附近邻域的边缘/线条的空间分布进行建模，从而获得对局部形状进行具有一定区分性与紧致性的描述。随后采用特征匹配的方法获得查询图像与待检索图像之间相似度。局部形状特征跨越了自然图像与非自然图像之间外观差异的鸿沟，同时排除背景信息的干扰，实现对局部内容进行检索。
(3)针对卡通图像互联网分享过程中的滥用问题，提出了基于Hough voting的卡通角色检测方法，供媒体分享网站对用户上传图像或视频进行过滤。首先采用人工分割的模板来学习表达卡通角色的特征词典及基于Hough voting的目标表达。随后对模板和待检测图像特征进行匹配基础上，根据目标的表达，在Hough voting空间中计算某个位置存在该卡通角色的后验概率，从而获得该角色出现的位置及相应的可能性。算法在对卡通图像的检测中显示出了其准确性及效率。
(4)提出了基于选择加密的卡通媒体分级预览方案，以在计算代价较低的情况下保证数据的机密性，并提供分级浏览的功能。首先基于薄板样条函数对已经定位于窗口内部的卡通角色精确提取外轮廓；随后基于混沌系统构造级联的序列密码算法对目标区域进行加密。选择加密的方法对核心内容进行了加密，而保留背景区域供人预览，提供了更好的用户体验性，提高了卡通媒体可推广性。
Nowadays, information technology changes human being’s life a lot. In thisprogress, knowledge-based economy and information technology influence eachother, and make greate advances. Cartoon industry is a typical knowledge-basedindustry, and gains a good opportunity for further developing. All the aspects ofcartoon industry, including the producing, broadcasting and consuming, benefit fromthe information technology and make a great progress. However, the informationtechnology also brings more complex copyright problems at the same time, whichmake the cartoon copyright protection an urgent problem.
The key issues of the copyright infringement problem in cartoon mainly lie onunauthorized browsing, sharing and copy during the cartoon broadcasting, spreadingand the application. Traditional copyright protection techniques, such aswatermarking and cryptography, are not sufficient for such complex problems.Obviously, cartoon characters are the key content of the cartoon media, which isalso the mostly frequently infringed part. Most of the infringements involve thecartoon characters, so the characters are the invariant theme in the infringements.Therefore, cartoon character protection is the key of the cartoon media copyrightprotection.
This thesis focuses on the copyright protection of cartoon media. Firstly, thespecialty of the cartoon image is discussed, and a basic viewpoint is proposed: shapeis the essential element to present the cartoon character and the similarity in shape isthe internal connection between same characters in different images. Secondly, edgeand curve detection methods are proposed to extract the shape in either natural andcartoon images. Then, a new local invariant shape feature is proposed to describethe cartoon characters in the image, in which the location, angle and scale ofcharacter are unknown. Based on this feature, the research on the cartoon characterindexing, detection and selective encryption are done.
The main contribution and innovation of this dissertation are described asfollows:
Firstly, the learning based edge detection methods for natural images arediscussed and then a new edge detection method based on Fuzzy Weighed SupportVector Machine for Regression is proposed to alleviate the label impression problemand noise. A fuzzy membership is applied to re-weigh the training sample accordingto the distance to the human labels, so that the samples with different reliability aretreated differently to alleviate the label imprecision problem. Then a curveextraction method for cartoon images is proposed to extract the shape in the cartoon image. The two types of curve in the cartoon image are defined and extractedseparately based on their different response to the first and the second orderdirectional derivative. The proposed method is more efficient and produces betterresults.
Secondly, a scale and rotation invariant local feature is proposed to describe thelocal shape, and used in the cartoon character retrieval in both the natural image andthe cartoon image. The feature locates at the scale invariant corner and use the shapecontext to describe the shape near the corner, and a feature matching method is usedto compute the similarity between the query and the image in the database. The usedof shape feature fill the gap between the appearance differences between naturalimage and cartoon image, and reduce the disturbance from the background.
Thirdly, a Hough voting based cartoon character detection method is studied topreventing the unauthorized sharing in the website. The visual shape words areextracted in the pre-segmented cartoon characters and then a Hough voting schemeis used to present the character. And then a posterior probability conditioned on theexistence of visual words is estimated in the Hough voting space. The proposedmethod is effective as the experimental results show.
Fourthly, selective encryption method is investigated in order to protect thecartoon from unauthorized copy. The Thin Plain Spline-Robust Point Matchingmethod is employed to localize the cartoon character accurately, and then a streamcipher based on multi-chaotic systems is used to encrypt and decrypt the characterregion. The selective encryption method encrypts the key content of the cartoon andleaves the others as plain for the preview, which can offer better user experiencewhich make cartoon publish more easily.
引文
[1] Naisbitt J.大趋势——改变我们生活的十个新方向[M].中国社会科学出版社,1984:1-20.
[2]牛夏牧.数字水印处理算法及测试技术[D].哈尔滨工业大学博士论文，2000:10-17.
[3]孙圣和,陆哲明,牛夏牧.数字水印技术及应用[M].北京：科学出版社，2004:13-35.
[4] Lian S. Multimedia Content Encryption: Techniques and Applications [M].New York: CRC Press,2008:12-20.
[5] Schneier B. Applied Cryptography (Second Edition): Protocals, Algorithm,and Source Code in C[M]. John Wiley&Sons, Inc,1996:23-29.
[6] Young R A. Simulation of Human Retinal Function With the GaussianDerivative Model [C]. IEEE Computer Society Conference on ComputerVision and Pattern Recognition,1986:564-569.
[7] Young R A. The Gaussian Derivative Theory of Spatial Vision: Analysis ofCortical Cell Receptive Field Line-Weighting Profiles [R]. Technical ReportGMR-4920, Computer Science Department, General Motors Research Lab,Warren, Michigan,1985:1-15.
[8] Young R A. The Gaussian Derivative Model for Spatial Vision: I. RetinalMechanisms [J]. Spatial Vision,1987,2(4):273-293.
[9] Lindeberg T. Scale-Space Theory in Computer Vision [M]. Dordrecht,Netherlands: Kluwer Academic Publishers,1994.
[10] Barlow H B. Possible Principles Underlying the Transformation of SensoryMessages [J]. Sensory communication,1961:217-234.
[11] Atick J J, Redlich A N. What Does the Retina Know about Natural Scenes [J].Neural Computation,1992(4):196-210.
[12] Ruderman D L. Bialek W. Statistics of Natural Images: Scaling in theWoods[J]. Physical Review Letters,1994,73(6):814-817.
[13] Simoncelli E P, Olshausen B A. A Natural Image Statistics and NeuralRepresentation[J]. Neuro Science,2001,24:1193-1216.
[14] Torralba A, Oliva A. Statistics of Natural Image Categories [J]. Network:Computation in Neural Systems,2003,14:391-412.
[15]罗莹.动漫卡通形象的知识产权法保护分析[D].华南理工硕士论文，2011:1-15.
[16]刘纯.基于演化经济学的动漫品牌价值评价与管理体系研究[D].中南大学博士论文,2012:34-47.
[17]张毓晋.基于内容的视觉信息检索[M].北京:科学出版社,2003:1-15
[18] Marr D, Hildreth E. Theory of Edge Detection [C], Proceeding of the RoyalSociety, B: Biological Science.1980,207(1167):187-217.
[19] Richard Szeliski. Computer Vision: Algorithms and Applications [M],Springer,2010:656-709.
[20] Roberts L. G. Machine Perception of Three-Dimensional Solids[M]. InOptical and Electro-Optical Information Processing, Cambridge: MIT Press,1965:1-10.
[21] Duda R O, Hart P. E. Pattern Classification and Scene Analysis [M]. NewYork: Wiley,1973:1-8.
[22] Prewitt J M S. Object Enhancement and Extraction [M]. In Picture Processingand Psychohistories, New York: Academic Press,1970:1-17.
[23]孙即祥,数字图像处理[M]，科学出版社，2004:123-137
[24] Marr D, Poggio T. A Computational Theory of Human Stereo Vision [C].Proceedings of the Royal Society of London B,2004:301-328.
[25] Canny J. A Computational Approach to Edge Detection [J]. IEEE Trans.Pattern Analysis and Machine Intelligence,1986,8(6):679-698.
[26] Rosenfeld A. Computer Vision: A Source of Models for Biological VisualProcessing [J]. IEEE Trans. on Biomed Engineer,1989,36(1):83-94.
[27] Carron T, Lambert P. Color Edge Detector Using Jointly Hue, Saturation andIntensity [C]. IEEE Conference on Image Process,1994:977-981.
[28] Scharcanski J. Edge Detection of Color Images Using DirectionalOperators[J]. IEEE Trans on Circuits and Systems for Video Technology,1997,7(2):397-401.
[29]吕明忠,罗鹏,高敦岳.一种基于色差的彩色图像的边缘检测方法[J].华东理工大学学报,2001,27(5):561-564.
[30] Evans A N, Liu X. A Morphological Gradient Approach to Color EdgeDetectio [C]. IEEE Trans on Image Processing,2006,15(6):1454-1463.
[31] Wen F, Hu M. B. Yuan. A Comparative Study on Color Edge Detection [C].IEEE Conference on Computer, Communication, Control. Power Engineering,2002(1):511-514.
[32] Zenzo S D. A Note on the Gradient of a Multi-Image[J]. Computer Vision，Graphics and Image Processing,1986,33(1):116-125.
[33] Chapron M. A Color Edge Detector Based on Dempster-Shafer Theory[C].IEEE Conference on. Image Process,2000:812-815.
[34] Chapron M. A Color Edge Detector Based on Statistical Rupture Tests[C].IEEE Conference on. Image Process,2000:820-823.
[35] Ruzon M A, Tomasi C. Color Edge Detection with the Compass Operator [C].Proceedings of the IEEE Computer Vision and Patten Recognition.FortCollins, CO, USA,1999,2:160-166.
[36] Martin D, Fowlkes C. J. Malik. Learning to Detect Natural Image Boundariesusing Local Brightness, Color and Texture Cues[J]. IEEE Trans. on PatternAnalysis and Machine Intelligence,2004,26(5):530-549.
[37] Dollar P, Tu Z, Belongie S. Supervised Learning of Edges and ObjectBoundaries [C]. IEEE Conference on Computer Vision and PatternRecognition,2006,2:1964-1971.
[38] Ren X, Fowlkes C, Malik J. Multi-scale Improves Boundary Detection inNatural Images[C]. European Conference on Computer Vision,2008:533-545.
[39] Ren X, Fowlkes C, Malik J. Learning Probabilistic Models for ContourCompletion in Natural Images [J]. International Journal of Computer Vision,Special Issue on Machine Learning for Vision, May2008,77(1-3):47-63.
[40] Zhu Q, Song G, Shi J. Untangling Cycles for Contour Grouping[C],International Conference on Computer Vision,2007:1-8.
[41] Maire M, Arbel′aez P, Fowlkes C, J Malik. Using Contours to Detect andLocalize Junctions in Natural Images[C]. IEEE Conference on ComputerVision and Pattern Recognition,2008:1-8.
[42] Shi J, Malik J. Normalized Cuts and Image Segmentation [J]. IEEE Trans. onPattern Analysis and Machine Intelligence,2000,22(8):888-905.
[43] Arbel′aez P, Maire M, Fowlkes C, Malik J. Contour Detection andHierarchical Image Segmentation [J]. IEEE Transactions on Pattern Analysisand Machine Intelligence,2011,33(5):898-916.
[44] Moravec H P. Towards Automatic Visual Obstacle Avoidance[C].5th JointConference on Artificial Intelligence,1977:584-584.
[45] Harris C, Stephens M. A Combined Corner and Edge Eetector [C].Proceedings of the4th Alvey Vision Conference.1988:147-151.
[46] Lindeberg T. Discrete Scale-space Theory and the Scale-space PrimalSketch[D]. Stockholm, Sweden: Royal Institute of Technology,1991:34-43.
[47] Lindeberg T. Scale-space for Discrete Signals[J]. IEEE Transactions onPatternAnalysis and Machine Intelligence,1990,12(3):234-254.
[48] Lindeberg T. Feature Detection with Automatic Scale Selection[J].International Journal of Computer Vision,1998,30(2):77-116.
[49] Lindeberg T. Scale-space Theory in Computer Vision [M]. Dordrecht,Netherlands: Kluwer Academic Publishers,1994:3-12.
[50] Mikolajczyk K, Schmi C. An Affine Invariant Interest Point Detector[C]. InEuropean Conference on Computer Vision.2002:28-31.
[51] Mikolajczyk K, Schmid C. Scale&Affine Invariant Interesting PointDetectors [C]. International Journal of Computer Vision.2004,60(1):63-86.
[52] Lowe D G. Object Recognition from Local Scale-Invariant Features[C].International Conference on Computer Vision,1999:1150-1157.
[53] Bay H, Tutelaars T, Van Gool L, SURF: Speeded-Up Robust Features [J].Computer Vision and Image Understanding,2008,110:404-417.
[54] Matas J, Chum O, Pajdla M. Robust Wide Baseline Stereo from MaximallyStable Extremal Regions[C]. British Machine Vision Conference2002:384-393.
[55] Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors [J].IEEE Transactions on Pattern Analysis and Machine Intelligence2005.27(10):1615-1630.
[56] Tuytelaars T, Mikolajczyk K. Local Invariant Feature Detectors: A Survey [J].Foundations and Trends in Computer Graphics and Vision,2007,3(3):177-280.
[57]王永明,王贵锦.图像局部不变性特征与描述[M].北京:国防工业出版社,2010:1-24.
[58] Belongie S, Malik J, Puzicha J. Shape Matching and Object RecognitionUsing Shape Contexts [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2002,24(24):509-521.
[59] Berg A C, Malik J. Shape Matching and Object Recognition using LowDistortion Correspondence [C]. IEEE Conference on Computer Vision andPattern Recognition,2005:26-33.
[60] Ferrari V, Tuytelaars T, Gool L V. Object Detection with Contour SegmentNetworks[C]. European Conference on Computer Vision,2006:14-28.
[61] Ferrari V, Fevrier L, Jurie F, Schmid C. Groups of Adjacent ContourSegments for Object Detection [J]. IEEE Transactions on Pattern Analysisand Machine Intelligence,2008,30(1):36-51.
[62] Ferrari V, Jurie F, Schmid C. Accurate Object Detection with DeformableShape Models Learnt from Images[C]. IEEE Conference on Computer Visionand Pattern Recognition,2007:1-8
[63] Swain M J, Ballard D H. Color Indexing[J]. International Journal ofComputer Vision,1991,7(1):11-32.
[64] Smeulders A W, Worring M, Santini S, Gupta A, Martin R. Content-BasedImage Retrieval at the End of the Early Years[J]. IEEE Transactions onPattern Analysis and Machine Intelligence,2000,22(12):1349-1380.
[65] Hafner J, Sawhney H. Efficient Color Histogram Indexing for Quadtraticfrom Distance Functions [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2001,17(7):729-736
[66] Stricke M, Orengo M. Similarity of Color Images[M], SPIE,1995,v2420:381-392
[67] Kasutani E, Yamada A. The MPEG-7Color Layout Descriptor: A CompactImage Feature Description for High-Speed Image/Video SegmentRetrieval[C]. IEEE International Conference on Image Processing,2001(1):674-677,
[68] Yamada A, Kasutani E, Ohta M. Visual Program Navigation System Based onSpatial Distribution of Color. IEEE International Conference on ConsumerElectronics,2000:280-281
[69] Li X. Image Retrieval Based on Perceptive Weighted Color blocks [J]. PatternRecognition Letters,2003,24(12):1935-1941.
[70] Yu H, Li M, Zhang H, Feng J. Color Texture Moments for Content-BasedImage Retrieval[C]. IEEE International Conference on Image Processing,2002,3:929-932
[71] Manning C D, Raghavan P, Schutze H. Introduction to Information Retrieval[M]. Cambridge: Cambridge University Press.2008:10-16.
[72] Robert A, Wang J, Laurent Y. Texture Classification Using Windowed FourierFilters [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(2):148-153.
[73] Laine A, Fan J. Texture Classification by Wavelet Packet Signatures[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,1993,15(11):1186-1191.
[74] Dunn D, Higgins W, Wakeley J. Texture Segmentation Using2-D GaborElementary Functions[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,1994,16(2):130-148.
[75] Chetverikov D, Hanbury A. Finding Defects in Texture Using Regularity andLocal Orientation [J]. Pattern Recognition,2002,35(10):2165-2180.
[76] Chen J, Jain A. A Structural Approach to Identify Defects in TextureImages[C]. Proceedings of IEEE International Conference on Systems, Man,and Cybernetics. IEEE1988:29-32.
[77] Livarinen J, Peura M, Srel J. Comparison of Combined Shape Descriptors forIrregular Object [C]. The British Machine Vision Conference,1997:430-439.
[78] Elghazal A. Basir O, Belkasim S. A Novel Curvature-Based Shape FourierDescriptor [C], IEEE International Conference on Image Processing.2008:953-956.
[79] Jacobs C E. Fast Multi-Resolution Image Querying[C]. Proceeding ofSIGGAPH,1995:277-286.
[80] Vailaya A, Zhong Y, Jain A. A Hierarchical System for Efficient ImageRetrieval[C]. International Conference on Pattern Recognition,1996:336-360.
[81] Mokhtarian F, Abbasi S, Kittler J. Efficient and Robust Retrieval by ShapeContent Through Curvature Scale Space[J]. Series on Software Engineeringand Knowledge Engineering,1997,8:51-58.
[82] Bimbo A, Pala P. Visual Image Retrieval by Elastic Matching of UserSketches [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,1997:121-132.
[83] Teh C H, Chin R.T. On Image Analysis by the Methods of Moments [J]. IEEETrans. on Pattern Analysis and Machine Intelligence,1988,10(4):496-513.
[84] Teague M. Image Analysis via the General Theory of Moments [J]. Journal ofthe Optical Society of America,1980,70(8):920-930.
[85] Mikolajczyk K, Schmid C. Indexing Based on Scale Invariant InterestPoints[C]. International Conference on Computer Vision.2001:525-531.
[86] Gool L V, Tuytelaars T, Turina A. Local Features for Image Retrieval [M].Chapter in Content-Based image and Video Retrieval, Kluwer AcademicPublishers,2001:21-41.
[87] Philbin, J, Chum O, Isard M, Sivic J, Zisserman A. Object Retrieval withLarge Vocabularies and Fast Spatial Matchin[C]. International Conference onComputer Vision and Pattern Recognition,2007:1-8.
[88] Philbin, J, Chum O, Sivic J, Isard M, Zisserman A. Lost in Quantization:Improving Particular Object Retrieval in Large Scale Image Databases[C].International Conference on Computer Vision and Pattern Recognition,2008:1-8.
[89] Deselaers T, Keysers D, Ney H. Features for Image Retrieval: AnExperimental Comparison[J]. Information Retrieval.2008,11(2):77-107.
[90] Cipolla R, Battiato S, Farinella G M. Computer Vision: Detection,Recognition and Reconstruction [M]. Springer,2010:445-460.
[91] Mundy J. L. Object Recognition in the Geometric era: Chapter in TowardCategory-Level Object Recognition, New York:Springer,2006:3-28.
[92] Murase H, Nayar S K. Visual Learning and Recognition of3-D Objects fromAppearance [J]. International Journal of Computer Vision,2005,14(1):5-24
[93] Rothganger F, Lazebnik S, Schmid C, Ponce J.3D Object Modeling andRecognition Using Local Affine-Invariant Image Descriptors and Multi-ViewSpatial Constraints [J]. International Journal of Computer Vision,2006,66(3):231-259.
[94] Ferrari V, Jurie F, Schmid C. From Images to Shape Models for ObjectDetection [J]. International Journal of Computer Vision,2010,87(3):284-303.
[95] Obdrzalek S. Matas J. Object Recognition Using Local Affine Frames onMaximally Stable Extremal Regions. Chapter in Toward Category-LevelObject Recognition [M]. New York: Springer,2006:83-104.
[96] Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[97] Rothganger F, Lazebnik S, Schmid C, Ponce J.3D Object Modeling andRecognition Using Local Affine-Invariant Image Descriptors and Multi-ViewSpatial Constraints [J]. International Journal of Computer Vision,66(3):231-259.
[98] Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski,Cédric Bray. Visual Categorization with Bags of Keypoints[C]. ECCVWorkshop on Statistical Learning in Computer Vision,2004:1-22
[99] Grauman K, Darrell T. The Pyramid Match Kernel: DiscriminativeClassification with Sets of Image Features[C]. IEEE InternationalConference on Computer Vision.2005,2:1458-1465.
[100]王利明.机器视觉中物体识别方法的研究与探讨[D].复旦大学博士论文,2009:3-8,67-70.
[101] Fergus R, Perona P, Zisserman A. Weakly Supervised Scale-InvariantLearning of Models for Visual Recognition [J]. International Journal ofComputer Vision,2007,71(3):273-303.
[102] Crandall D, Huttenlocher D. Composite Models of Objects and Scenes forCategory Recognition [C]. IEEE Conference on Computer Vision and PatternRecognition,2007:1-8.
[103] Crandall D, Felzenszwalb P, Huttenlocher D. Spatial Priors for Part-BasedRecognition Using Statistical Models[C]. IEEE Conference on ComputerVision and Pattern Recognition,2005:10-17.
[104] Felzenszwalb P F, Huttenlocher D P. Pictorial Structures for ObjectRecognition [J]. International Journal of Computer Vision,2005:61(1):55-79.
[105] Bouchard G, Triggs B. Hierarchical Part-Based Visual ObjectCategorization. IEEE Conference on Computer Vision and PatternRecognition2005:709-714.
[106] Carneiro G, Lowe D. Sparse Flexible Models of Local Features [C].European Conference on Computer Vision,2006:29-43.
[107] Podesser M, Schmidt H P, Uhl A. Selective Bitplane Encryption for SecureTransmission of Image Data in Mobile Environments[C]. IEEE Nordic SignalProcessing Symposium (NORSIG2002)[CD-ROM].
[108] Pommer A, Uhl A. Selective Encryption of Wavelet-Packet EncodedImage Data: Efficiency and Security[J]. Communications and MultimediaSecurity,2003:194-204.
[109] Norcen R, Uhl A. Selective Encryption of the JPEG2000Bitstream.International Federation for Information Processing. Lecture Notes inComputer Science,2003,2828:194-204.
[110] Shi C, Bhargava B. A Fast MPEG Video Encryption Algorithm. ACMInternational Multimedia Conference.2008:81-88.
[111] Zeng W, Lei S. Efficient Frequency Domain Selective Scrambling ofDigital Video [J]. IEEE Transactions on Multimedia,2003,5(1):118-129.
[112] Lian S, Sun J, Wang Z. Perceptual Cryptography on SPIHT CompressedImages or Videos[C]. IEEE International Conference on Multimedia andExpo2004(3):2195-2198.
[113] Zhou Y, Panetta K, Cherukuri R. Selective Object Encryption for PrivacyProtection[C]. SPIE Defense, Security, and Sensing,2009:73510F-73510F-10.
[114] Bhatnagar G, Jonathan Wu Q M. Selective Image Encryption Based onPixels of Interest and Singular value Decomposition [J]. Digital SignalProcessing,2012:648-663.
[115] Feng D, Chen J, Liu J, et al. BLESS: Object Level Encryption Security forObject-based Storage System[J]. Mathematical and Computer Modeling,2012,55(1):188-197.
[116] Dufaux F, Ebrahimi T. Scrambling for Video Surveillance with Privacy[C].IEEE Conference on Computer Vision and Pattern Recognition Workshop,2006:160-160.
[117] Dufaux F, Ebrahimi T. Scrambling for Privacy Protection in VideoSurveillance Systems [J]. IEEE Transactions on Circuits and Systems forVideo Technology,2008,18(8):1168-1174.
[118]韩琦,王志芳,牛夏牧,李琼.针对索引图像的人脸区域分级加密算法.电子学报,2008,36(B12):25-29.
[119] Vapnik V. The Nature of Statistic Learning Theory [M]. New York:Springer,1996:33-57.
[120] Guyon I, Boser B, Vapnik V. Automatic Capacity Tuning of Very LargeVC-Dimension Classifier [J]. Neural Inform. Process Systems,2002,5:147-155.
[121] Lin C F, Wang S D. Fuzzy Support Vector Machines[J], IEEE Transactionson Neural Networks,2002,13(2):464-471.
[122] PeiYi Hao. Fuzzy One-class Support Vector Machines[J], Fuzzy Set andSystems,2008,159:2317-2336.
[123] Jiang X, Zhang Y, Lv J C. Fuzzy SVM with A New Fuzzy MembershipFunction. Neural Computing and Application,2006,15:268-276.
[124] Lin C F, Wang S D. Training Algorithms for Fuzzy Support VectorMachines with Noisy Data [J]. Pattern Recognition Letters,2004,25:1647-1656.
[125] Lin C F, Wang S D. Fuzzy Support Vector Machines with AutomaticMembership Setting[J], StudFuzz,2005,17:233-254.
[126] Wang L, Fu D M. Efficient Support Vector Regression with WeightedConstraints [J], LNEE,2012,133:607-615.
[127] Wen W, Hao Z, Yang XW. A Heuristic Weight-Setting Strategy andIteratively Updating Algorithm for Weighted Least-Squares Support VectorRegression[J]. Neurocomputing,2008,71:3096-3103.
[128] Hao P Y, Chiang J H. Fuzzy Regression Analysis by Support VectorLearning Approach[J]. IEEE Transactions on Fuzzy Systems,16(2):428-441.
[129] Hong D H, Hwang C. Support Vector Fuzzy Regression Machines[J].Fuzzy Sets and Systems,2003,138:271-281.
[130] Zhang S H, Chen T, Zhang Y F, Hu S M, Martin R R. Vectorizing CartoonAnimations[J]. IEEE Transactions on Visualization and Computer Graphics,2009,15(4):618-629.
[131] Steger C. An Unbiased Detector of Curvilinear Structures[J]. IEEE Trans.Pattern Analysis and Machine Intelligence,1998,20(2):113-125.
[132] Sykora D, Buriánek J, Zára J. Segmentation of Black-and-White Cartoons[C] In Proceedings of the19th Spring Conference on Computer Graphics,2003:223-230
[133] Sykora D, Buriánek J, Zára J. Sketching Cartoons by Example[C]. SecondEurographics Workshop Sketch-Based Interfaces and Modeling,2005:27-34.
[134] Yang P, Wang G Z. Unbiased Curvilinear Structure Extraction for CartoonImages [C]. Eighth International Symposium on Voronoi Diagrams inScience and Engineering,2011:220-227.
[135] Huang M C, Yang M, Liu F, Wu E H. Stroke Extraction in Cartoon ImagesUsing Edge-Enhanced Isotropic Nonlinear Filter[C]. Proceedings of the9thACM SIGGRAPH Conference on Virtual-Reality Continuum and itsApplications in Industry.2010:33-38.
[136] Deselaers T, Keysers D, Ney H. Discriminative Training for ObjectRecognition Using Image Patches [C]. IEEE Conference on Computer Visionand Pattern Recognition,2005,2:157-162.
[137] Jonker R, Volgenant A. A Shortest Augmenting Path Algorithm for Denseand Sparse Linear Assignment Problems[J]. Computing,1987,38(4):325-340.
[138] Illingworth J, Kittler J. A Survey of the Hough Transform [J]. ComputerVision, Graphics, and Image Processing,1988,44(1):87-116.
[139] Leibe B, Leonardis A, Schiele B. Combined Object Categorization andSegmentation with An Implicit Shape Model[C]. ECCV Workshop onStatistical Learning in Computer Vision,2004:17-32.
[140] Opelt A, Pinz A, Zisserman A. A Boundary-Fragment-Model for ObjectDetection [C] European Conferenceon Computer Vision,2006:575-588.
[141] Yarlagadda P, Monroy A, Ommer B. Voting by Grouping Dependent Parts[C] European Conferenceon Computer Vision,2010:197-210.
[142] Maji S, Malik J. Object Detection Using a Max-Margin Hough Transform[C]. IEEE Conference on Computer Vision and Pattern Recognition,2009:1038-1045.
[143] Gall J, Lempitsky V. Class-Specific Hough Forests for Object Detection[M]. Decision Forests for Computer Vision and Medical Image Analysis.Springer London,2013:143-157.
[144] Gall J, Yao A, Razavi N, et al. Hough Forests for Object Detection,Tracking, and Action Recognition [J]. IEEE Transactions on Pattern Analysisand Machine Intelligence,2011,33(11):2188-2202.
[145] Deselaers T, Keysers D, Ney H. Features for Image Retrieval: AnExperimental Comparison [J]. Information Retrieval,2008,11(2):77-107.
[146] Deselaers T, Keysers D, Ney H. Features for Image Retrieval: AQuantitative Comparison [M]. Pattern Recognition. Springer,2004:228-236.
[147] Duda R O, Hart P E, Stork D G. Pattern Classification[M]. JohnWiley&Sons,2001:442-447.
[148] Yang J. The Thin Plate Spline Robust Point Matching (TPS-RPM)Algorithm: A Revisit [J]. Pattern Recognition Letters,2011,32(7):910-918.
[149] Chui H, Rangarajan A. A New Point Matching Algorithm for Non-RigidRegistration [J]. Computer Vision and Image Understanding,2003,89(2):114-141.
[150] Chui H, Rangarajan A. A New Algorithm for Non-Rigid PointMatching[C]. IEEE Conference on Computer Vision and Pattern Recognition,2000,2:44-51.
[151] Shannon C.E. Communication Theory of Secrecy Systems[J]. Bell systemTechnology Journal.1949,28:656-715.
[152] Maurer U. A Provably-Secure Strongly-Randomized Cipher[C]. Advancesin Cryptology,2006:361-373.
[153] Gao H, Zhang Y, Liang S. A New Chaotic Algorithm for Image Encryption[J]. Chaos, Solitons&Fractals,2006,29(2):393-399.
[154] Feigenbaum M J. Quantitative Universality for A Class of NonlinearTransformations[J]. Journal of statistical physics,1978,19(1):25-52.
[155] Amin M, Faragallah O, Abd El-Latif A. A Chaotic Block Cipher Algorithmfor Image Cryptosystems[J]. Communications in Nonlinear Science andNumerical Simulation,2010,15(11):3484-3497.
[156] Wenbo Mao,现代密码学理论与实践:英文版[M].电子工业出版社,2004:118-140.
[2]牛夏牧.数字水印处理算法及测试技术[D].哈尔滨工业大学博士论文，2000:10-17.
[3]孙圣和,陆哲明,牛夏牧.数字水印技术及应用[M].北京：科学出版社，2004:13-35.
[4] Lian S. Multimedia Content Encryption: Techniques and Applications [M].New York: CRC Press,2008:12-20.
[5] Schneier B. Applied Cryptography (Second Edition): Protocals, Algorithm,and Source Code in C[M]. John Wiley&Sons, Inc,1996:23-29.
[6] Young R A. Simulation of Human Retinal Function With the GaussianDerivative Model [C]. IEEE Computer Society Conference on ComputerVision and Pattern Recognition,1986:564-569.
[7] Young R A. The Gaussian Derivative Theory of Spatial Vision: Analysis ofCortical Cell Receptive Field Line-Weighting Profiles [R]. Technical ReportGMR-4920, Computer Science Department, General Motors Research Lab,Warren, Michigan,1985:1-15.
[8] Young R A. The Gaussian Derivative Model for Spatial Vision: I. RetinalMechanisms [J]. Spatial Vision,1987,2(4):273-293.
[9] Lindeberg T. Scale-Space Theory in Computer Vision [M]. Dordrecht,Netherlands: Kluwer Academic Publishers,1994.
[10] Barlow H B. Possible Principles Underlying the Transformation of SensoryMessages [J]. Sensory communication,1961:217-234.
[11] Atick J J, Redlich A N. What Does the Retina Know about Natural Scenes [J].Neural Computation,1992(4):196-210.
[12] Ruderman D L. Bialek W. Statistics of Natural Images: Scaling in theWoods[J]. Physical Review Letters,1994,73(6):814-817.
[13] Simoncelli E P, Olshausen B A. A Natural Image Statistics and NeuralRepresentation[J]. Neuro Science,2001,24:1193-1216.
[14] Torralba A, Oliva A. Statistics of Natural Image Categories [J]. Network:Computation in Neural Systems,2003,14:391-412.
[15]罗莹.动漫卡通形象的知识产权法保护分析[D].华南理工硕士论文，2011:1-15.
[16]刘纯.基于演化经济学的动漫品牌价值评价与管理体系研究[D].中南大学博士论文,2012:34-47.
[17]张毓晋.基于内容的视觉信息检索[M].北京:科学出版社,2003:1-15
[18] Marr D, Hildreth E. Theory of Edge Detection [C], Proceeding of the RoyalSociety, B: Biological Science.1980,207(1167):187-217.
[19] Richard Szeliski. Computer Vision: Algorithms and Applications [M],Springer,2010:656-709.
[20] Roberts L. G. Machine Perception of Three-Dimensional Solids[M]. InOptical and Electro-Optical Information Processing, Cambridge: MIT Press,1965:1-10.
[21] Duda R O, Hart P. E. Pattern Classification and Scene Analysis [M]. NewYork: Wiley,1973:1-8.
[22] Prewitt J M S. Object Enhancement and Extraction [M]. In Picture Processingand Psychohistories, New York: Academic Press,1970:1-17.
[23]孙即祥,数字图像处理[M]，科学出版社，2004:123-137
[24] Marr D, Poggio T. A Computational Theory of Human Stereo Vision [C].Proceedings of the Royal Society of London B,2004:301-328.
[25] Canny J. A Computational Approach to Edge Detection [J]. IEEE Trans.Pattern Analysis and Machine Intelligence,1986,8(6):679-698.
[26] Rosenfeld A. Computer Vision: A Source of Models for Biological VisualProcessing [J]. IEEE Trans. on Biomed Engineer,1989,36(1):83-94.
[27] Carron T, Lambert P. Color Edge Detector Using Jointly Hue, Saturation andIntensity [C]. IEEE Conference on Image Process,1994:977-981.
[28] Scharcanski J. Edge Detection of Color Images Using DirectionalOperators[J]. IEEE Trans on Circuits and Systems for Video Technology,1997,7(2):397-401.
[29]吕明忠,罗鹏,高敦岳.一种基于色差的彩色图像的边缘检测方法[J].华东理工大学学报,2001,27(5):561-564.
[30] Evans A N, Liu X. A Morphological Gradient Approach to Color EdgeDetectio [C]. IEEE Trans on Image Processing,2006,15(6):1454-1463.
[31] Wen F, Hu M. B. Yuan. A Comparative Study on Color Edge Detection [C].IEEE Conference on Computer, Communication, Control. Power Engineering,2002(1):511-514.
[32] Zenzo S D. A Note on the Gradient of a Multi-Image[J]. Computer Vision，Graphics and Image Processing,1986,33(1):116-125.
[33] Chapron M. A Color Edge Detector Based on Dempster-Shafer Theory[C].IEEE Conference on. Image Process,2000:812-815.
[34] Chapron M. A Color Edge Detector Based on Statistical Rupture Tests[C].IEEE Conference on. Image Process,2000:820-823.
[35] Ruzon M A, Tomasi C. Color Edge Detection with the Compass Operator [C].Proceedings of the IEEE Computer Vision and Patten Recognition.FortCollins, CO, USA,1999,2:160-166.
[36] Martin D, Fowlkes C. J. Malik. Learning to Detect Natural Image Boundariesusing Local Brightness, Color and Texture Cues[J]. IEEE Trans. on PatternAnalysis and Machine Intelligence,2004,26(5):530-549.
[37] Dollar P, Tu Z, Belongie S. Supervised Learning of Edges and ObjectBoundaries [C]. IEEE Conference on Computer Vision and PatternRecognition,2006,2:1964-1971.
[38] Ren X, Fowlkes C, Malik J. Multi-scale Improves Boundary Detection inNatural Images[C]. European Conference on Computer Vision,2008:533-545.
[39] Ren X, Fowlkes C, Malik J. Learning Probabilistic Models for ContourCompletion in Natural Images [J]. International Journal of Computer Vision,Special Issue on Machine Learning for Vision, May2008,77(1-3):47-63.
[40] Zhu Q, Song G, Shi J. Untangling Cycles for Contour Grouping[C],International Conference on Computer Vision,2007:1-8.
[41] Maire M, Arbel′aez P, Fowlkes C, J Malik. Using Contours to Detect andLocalize Junctions in Natural Images[C]. IEEE Conference on ComputerVision and Pattern Recognition,2008:1-8.
[42] Shi J, Malik J. Normalized Cuts and Image Segmentation [J]. IEEE Trans. onPattern Analysis and Machine Intelligence,2000,22(8):888-905.
[43] Arbel′aez P, Maire M, Fowlkes C, Malik J. Contour Detection andHierarchical Image Segmentation [J]. IEEE Transactions on Pattern Analysisand Machine Intelligence,2011,33(5):898-916.
[44] Moravec H P. Towards Automatic Visual Obstacle Avoidance[C].5th JointConference on Artificial Intelligence,1977:584-584.
[45] Harris C, Stephens M. A Combined Corner and Edge Eetector [C].Proceedings of the4th Alvey Vision Conference.1988:147-151.
[46] Lindeberg T. Discrete Scale-space Theory and the Scale-space PrimalSketch[D]. Stockholm, Sweden: Royal Institute of Technology,1991:34-43.
[47] Lindeberg T. Scale-space for Discrete Signals[J]. IEEE Transactions onPatternAnalysis and Machine Intelligence,1990,12(3):234-254.
[48] Lindeberg T. Feature Detection with Automatic Scale Selection[J].International Journal of Computer Vision,1998,30(2):77-116.
[49] Lindeberg T. Scale-space Theory in Computer Vision [M]. Dordrecht,Netherlands: Kluwer Academic Publishers,1994:3-12.
[50] Mikolajczyk K, Schmi C. An Affine Invariant Interest Point Detector[C]. InEuropean Conference on Computer Vision.2002:28-31.
[51] Mikolajczyk K, Schmid C. Scale&Affine Invariant Interesting PointDetectors [C]. International Journal of Computer Vision.2004,60(1):63-86.
[52] Lowe D G. Object Recognition from Local Scale-Invariant Features[C].International Conference on Computer Vision,1999:1150-1157.
[53] Bay H, Tutelaars T, Van Gool L, SURF: Speeded-Up Robust Features [J].Computer Vision and Image Understanding,2008,110:404-417.
[54] Matas J, Chum O, Pajdla M. Robust Wide Baseline Stereo from MaximallyStable Extremal Regions[C]. British Machine Vision Conference2002:384-393.
[55] Mikolajczyk K, Schmid C. A Performance Evaluation of Local Descriptors [J].IEEE Transactions on Pattern Analysis and Machine Intelligence2005.27(10):1615-1630.
[56] Tuytelaars T, Mikolajczyk K. Local Invariant Feature Detectors: A Survey [J].Foundations and Trends in Computer Graphics and Vision,2007,3(3):177-280.
[57]王永明,王贵锦.图像局部不变性特征与描述[M].北京:国防工业出版社,2010:1-24.
[58] Belongie S, Malik J, Puzicha J. Shape Matching and Object RecognitionUsing Shape Contexts [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2002,24(24):509-521.
[59] Berg A C, Malik J. Shape Matching and Object Recognition using LowDistortion Correspondence [C]. IEEE Conference on Computer Vision andPattern Recognition,2005:26-33.
[60] Ferrari V, Tuytelaars T, Gool L V. Object Detection with Contour SegmentNetworks[C]. European Conference on Computer Vision,2006:14-28.
[61] Ferrari V, Fevrier L, Jurie F, Schmid C. Groups of Adjacent ContourSegments for Object Detection [J]. IEEE Transactions on Pattern Analysisand Machine Intelligence,2008,30(1):36-51.
[62] Ferrari V, Jurie F, Schmid C. Accurate Object Detection with DeformableShape Models Learnt from Images[C]. IEEE Conference on Computer Visionand Pattern Recognition,2007:1-8
[63] Swain M J, Ballard D H. Color Indexing[J]. International Journal ofComputer Vision,1991,7(1):11-32.
[64] Smeulders A W, Worring M, Santini S, Gupta A, Martin R. Content-BasedImage Retrieval at the End of the Early Years[J]. IEEE Transactions onPattern Analysis and Machine Intelligence,2000,22(12):1349-1380.
[65] Hafner J, Sawhney H. Efficient Color Histogram Indexing for Quadtraticfrom Distance Functions [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,2001,17(7):729-736
[66] Stricke M, Orengo M. Similarity of Color Images[M], SPIE,1995,v2420:381-392
[67] Kasutani E, Yamada A. The MPEG-7Color Layout Descriptor: A CompactImage Feature Description for High-Speed Image/Video SegmentRetrieval[C]. IEEE International Conference on Image Processing,2001(1):674-677,
[68] Yamada A, Kasutani E, Ohta M. Visual Program Navigation System Based onSpatial Distribution of Color. IEEE International Conference on ConsumerElectronics,2000:280-281
[69] Li X. Image Retrieval Based on Perceptive Weighted Color blocks [J]. PatternRecognition Letters,2003,24(12):1935-1941.
[70] Yu H, Li M, Zhang H, Feng J. Color Texture Moments for Content-BasedImage Retrieval[C]. IEEE International Conference on Image Processing,2002,3:929-932
[71] Manning C D, Raghavan P, Schutze H. Introduction to Information Retrieval[M]. Cambridge: Cambridge University Press.2008:10-16.
[72] Robert A, Wang J, Laurent Y. Texture Classification Using Windowed FourierFilters [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(2):148-153.
[73] Laine A, Fan J. Texture Classification by Wavelet Packet Signatures[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,1993,15(11):1186-1191.
[74] Dunn D, Higgins W, Wakeley J. Texture Segmentation Using2-D GaborElementary Functions[J]. IEEE Transactions on Pattern Analysis andMachine Intelligence,1994,16(2):130-148.
[75] Chetverikov D, Hanbury A. Finding Defects in Texture Using Regularity andLocal Orientation [J]. Pattern Recognition,2002,35(10):2165-2180.
[76] Chen J, Jain A. A Structural Approach to Identify Defects in TextureImages[C]. Proceedings of IEEE International Conference on Systems, Man,and Cybernetics. IEEE1988:29-32.
[77] Livarinen J, Peura M, Srel J. Comparison of Combined Shape Descriptors forIrregular Object [C]. The British Machine Vision Conference,1997:430-439.
[78] Elghazal A. Basir O, Belkasim S. A Novel Curvature-Based Shape FourierDescriptor [C], IEEE International Conference on Image Processing.2008:953-956.
[79] Jacobs C E. Fast Multi-Resolution Image Querying[C]. Proceeding ofSIGGAPH,1995:277-286.
[80] Vailaya A, Zhong Y, Jain A. A Hierarchical System for Efficient ImageRetrieval[C]. International Conference on Pattern Recognition,1996:336-360.
[81] Mokhtarian F, Abbasi S, Kittler J. Efficient and Robust Retrieval by ShapeContent Through Curvature Scale Space[J]. Series on Software Engineeringand Knowledge Engineering,1997,8:51-58.
[82] Bimbo A, Pala P. Visual Image Retrieval by Elastic Matching of UserSketches [J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,1997:121-132.
[83] Teh C H, Chin R.T. On Image Analysis by the Methods of Moments [J]. IEEETrans. on Pattern Analysis and Machine Intelligence,1988,10(4):496-513.
[84] Teague M. Image Analysis via the General Theory of Moments [J]. Journal ofthe Optical Society of America,1980,70(8):920-930.
[85] Mikolajczyk K, Schmid C. Indexing Based on Scale Invariant InterestPoints[C]. International Conference on Computer Vision.2001:525-531.
[86] Gool L V, Tuytelaars T, Turina A. Local Features for Image Retrieval [M].Chapter in Content-Based image and Video Retrieval, Kluwer AcademicPublishers,2001:21-41.
[87] Philbin, J, Chum O, Isard M, Sivic J, Zisserman A. Object Retrieval withLarge Vocabularies and Fast Spatial Matchin[C]. International Conference onComputer Vision and Pattern Recognition,2007:1-8.
[88] Philbin, J, Chum O, Sivic J, Isard M, Zisserman A. Lost in Quantization:Improving Particular Object Retrieval in Large Scale Image Databases[C].International Conference on Computer Vision and Pattern Recognition,2008:1-8.
[89] Deselaers T, Keysers D, Ney H. Features for Image Retrieval: AnExperimental Comparison[J]. Information Retrieval.2008,11(2):77-107.
[90] Cipolla R, Battiato S, Farinella G M. Computer Vision: Detection,Recognition and Reconstruction [M]. Springer,2010:445-460.
[91] Mundy J. L. Object Recognition in the Geometric era: Chapter in TowardCategory-Level Object Recognition, New York:Springer,2006:3-28.
[92] Murase H, Nayar S K. Visual Learning and Recognition of3-D Objects fromAppearance [J]. International Journal of Computer Vision,2005,14(1):5-24
[93] Rothganger F, Lazebnik S, Schmid C, Ponce J.3D Object Modeling andRecognition Using Local Affine-Invariant Image Descriptors and Multi-ViewSpatial Constraints [J]. International Journal of Computer Vision,2006,66(3):231-259.
[94] Ferrari V, Jurie F, Schmid C. From Images to Shape Models for ObjectDetection [J]. International Journal of Computer Vision,2010,87(3):284-303.
[95] Obdrzalek S. Matas J. Object Recognition Using Local Affine Frames onMaximally Stable Extremal Regions. Chapter in Toward Category-LevelObject Recognition [M]. New York: Springer,2006:83-104.
[96] Lowe D G. Distinctive Image Features from Scale-Invariant Keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[97] Rothganger F, Lazebnik S, Schmid C, Ponce J.3D Object Modeling andRecognition Using Local Affine-Invariant Image Descriptors and Multi-ViewSpatial Constraints [J]. International Journal of Computer Vision,66(3):231-259.
[98] Gabriella Csurka, Christopher R. Dance, Lixin Fan, Jutta Willamowski,Cédric Bray. Visual Categorization with Bags of Keypoints[C]. ECCVWorkshop on Statistical Learning in Computer Vision,2004:1-22
[99] Grauman K, Darrell T. The Pyramid Match Kernel: DiscriminativeClassification with Sets of Image Features[C]. IEEE InternationalConference on Computer Vision.2005,2:1458-1465.
[100]王利明.机器视觉中物体识别方法的研究与探讨[D].复旦大学博士论文,2009:3-8,67-70.
[101] Fergus R, Perona P, Zisserman A. Weakly Supervised Scale-InvariantLearning of Models for Visual Recognition [J]. International Journal ofComputer Vision,2007,71(3):273-303.
[102] Crandall D, Huttenlocher D. Composite Models of Objects and Scenes forCategory Recognition [C]. IEEE Conference on Computer Vision and PatternRecognition,2007:1-8.
[103] Crandall D, Felzenszwalb P, Huttenlocher D. Spatial Priors for Part-BasedRecognition Using Statistical Models[C]. IEEE Conference on ComputerVision and Pattern Recognition,2005:10-17.
[104] Felzenszwalb P F, Huttenlocher D P. Pictorial Structures for ObjectRecognition [J]. International Journal of Computer Vision,2005:61(1):55-79.
[105] Bouchard G, Triggs B. Hierarchical Part-Based Visual ObjectCategorization. IEEE Conference on Computer Vision and PatternRecognition2005:709-714.
[106] Carneiro G, Lowe D. Sparse Flexible Models of Local Features [C].European Conference on Computer Vision,2006:29-43.
[107] Podesser M, Schmidt H P, Uhl A. Selective Bitplane Encryption for SecureTransmission of Image Data in Mobile Environments[C]. IEEE Nordic SignalProcessing Symposium (NORSIG2002)[CD-ROM].
[108] Pommer A, Uhl A. Selective Encryption of Wavelet-Packet EncodedImage Data: Efficiency and Security[J]. Communications and MultimediaSecurity,2003:194-204.
[109] Norcen R, Uhl A. Selective Encryption of the JPEG2000Bitstream.International Federation for Information Processing. Lecture Notes inComputer Science,2003,2828:194-204.
[110] Shi C, Bhargava B. A Fast MPEG Video Encryption Algorithm. ACMInternational Multimedia Conference.2008:81-88.
[111] Zeng W, Lei S. Efficient Frequency Domain Selective Scrambling ofDigital Video [J]. IEEE Transactions on Multimedia,2003,5(1):118-129.
[112] Lian S, Sun J, Wang Z. Perceptual Cryptography on SPIHT CompressedImages or Videos[C]. IEEE International Conference on Multimedia andExpo2004(3):2195-2198.
[113] Zhou Y, Panetta K, Cherukuri R. Selective Object Encryption for PrivacyProtection[C]. SPIE Defense, Security, and Sensing,2009:73510F-73510F-10.
[114] Bhatnagar G, Jonathan Wu Q M. Selective Image Encryption Based onPixels of Interest and Singular value Decomposition [J]. Digital SignalProcessing,2012:648-663.
[115] Feng D, Chen J, Liu J, et al. BLESS: Object Level Encryption Security forObject-based Storage System[J]. Mathematical and Computer Modeling,2012,55(1):188-197.
[116] Dufaux F, Ebrahimi T. Scrambling for Video Surveillance with Privacy[C].IEEE Conference on Computer Vision and Pattern Recognition Workshop,2006:160-160.
[117] Dufaux F, Ebrahimi T. Scrambling for Privacy Protection in VideoSurveillance Systems [J]. IEEE Transactions on Circuits and Systems forVideo Technology,2008,18(8):1168-1174.
[118]韩琦,王志芳,牛夏牧,李琼.针对索引图像的人脸区域分级加密算法.电子学报,2008,36(B12):25-29.
[119] Vapnik V. The Nature of Statistic Learning Theory [M]. New York:Springer,1996:33-57.
[120] Guyon I, Boser B, Vapnik V. Automatic Capacity Tuning of Very LargeVC-Dimension Classifier [J]. Neural Inform. Process Systems,2002,5:147-155.
[121] Lin C F, Wang S D. Fuzzy Support Vector Machines[J], IEEE Transactionson Neural Networks,2002,13(2):464-471.
[122] PeiYi Hao. Fuzzy One-class Support Vector Machines[J], Fuzzy Set andSystems,2008,159:2317-2336.
[123] Jiang X, Zhang Y, Lv J C. Fuzzy SVM with A New Fuzzy MembershipFunction. Neural Computing and Application,2006,15:268-276.
[124] Lin C F, Wang S D. Training Algorithms for Fuzzy Support VectorMachines with Noisy Data [J]. Pattern Recognition Letters,2004,25:1647-1656.
[125] Lin C F, Wang S D. Fuzzy Support Vector Machines with AutomaticMembership Setting[J], StudFuzz,2005,17:233-254.
[126] Wang L, Fu D M. Efficient Support Vector Regression with WeightedConstraints [J], LNEE,2012,133:607-615.
[127] Wen W, Hao Z, Yang XW. A Heuristic Weight-Setting Strategy andIteratively Updating Algorithm for Weighted Least-Squares Support VectorRegression[J]. Neurocomputing,2008,71:3096-3103.
[128] Hao P Y, Chiang J H. Fuzzy Regression Analysis by Support VectorLearning Approach[J]. IEEE Transactions on Fuzzy Systems,16(2):428-441.
[129] Hong D H, Hwang C. Support Vector Fuzzy Regression Machines[J].Fuzzy Sets and Systems,2003,138:271-281.
[130] Zhang S H, Chen T, Zhang Y F, Hu S M, Martin R R. Vectorizing CartoonAnimations[J]. IEEE Transactions on Visualization and Computer Graphics,2009,15(4):618-629.
[131] Steger C. An Unbiased Detector of Curvilinear Structures[J]. IEEE Trans.Pattern Analysis and Machine Intelligence,1998,20(2):113-125.
[132] Sykora D, Buriánek J, Zára J. Segmentation of Black-and-White Cartoons[C] In Proceedings of the19th Spring Conference on Computer Graphics,2003:223-230
[133] Sykora D, Buriánek J, Zára J. Sketching Cartoons by Example[C]. SecondEurographics Workshop Sketch-Based Interfaces and Modeling,2005:27-34.
[134] Yang P, Wang G Z. Unbiased Curvilinear Structure Extraction for CartoonImages [C]. Eighth International Symposium on Voronoi Diagrams inScience and Engineering,2011:220-227.
[135] Huang M C, Yang M, Liu F, Wu E H. Stroke Extraction in Cartoon ImagesUsing Edge-Enhanced Isotropic Nonlinear Filter[C]. Proceedings of the9thACM SIGGRAPH Conference on Virtual-Reality Continuum and itsApplications in Industry.2010:33-38.
[136] Deselaers T, Keysers D, Ney H. Discriminative Training for ObjectRecognition Using Image Patches [C]. IEEE Conference on Computer Visionand Pattern Recognition,2005,2:157-162.
[137] Jonker R, Volgenant A. A Shortest Augmenting Path Algorithm for Denseand Sparse Linear Assignment Problems[J]. Computing,1987,38(4):325-340.
[138] Illingworth J, Kittler J. A Survey of the Hough Transform [J]. ComputerVision, Graphics, and Image Processing,1988,44(1):87-116.
[139] Leibe B, Leonardis A, Schiele B. Combined Object Categorization andSegmentation with An Implicit Shape Model[C]. ECCV Workshop onStatistical Learning in Computer Vision,2004:17-32.
[140] Opelt A, Pinz A, Zisserman A. A Boundary-Fragment-Model for ObjectDetection [C] European Conferenceon Computer Vision,2006:575-588.
[141] Yarlagadda P, Monroy A, Ommer B. Voting by Grouping Dependent Parts[C] European Conferenceon Computer Vision,2010:197-210.
[142] Maji S, Malik J. Object Detection Using a Max-Margin Hough Transform[C]. IEEE Conference on Computer Vision and Pattern Recognition,2009:1038-1045.
[143] Gall J, Lempitsky V. Class-Specific Hough Forests for Object Detection[M]. Decision Forests for Computer Vision and Medical Image Analysis.Springer London,2013:143-157.
[144] Gall J, Yao A, Razavi N, et al. Hough Forests for Object Detection,Tracking, and Action Recognition [J]. IEEE Transactions on Pattern Analysisand Machine Intelligence,2011,33(11):2188-2202.
[145] Deselaers T, Keysers D, Ney H. Features for Image Retrieval: AnExperimental Comparison [J]. Information Retrieval,2008,11(2):77-107.
[146] Deselaers T, Keysers D, Ney H. Features for Image Retrieval: AQuantitative Comparison [M]. Pattern Recognition. Springer,2004:228-236.
[147] Duda R O, Hart P E, Stork D G. Pattern Classification[M]. JohnWiley&Sons,2001:442-447.
[148] Yang J. The Thin Plate Spline Robust Point Matching (TPS-RPM)Algorithm: A Revisit [J]. Pattern Recognition Letters,2011,32(7):910-918.
[149] Chui H, Rangarajan A. A New Point Matching Algorithm for Non-RigidRegistration [J]. Computer Vision and Image Understanding,2003,89(2):114-141.
[150] Chui H, Rangarajan A. A New Algorithm for Non-Rigid PointMatching[C]. IEEE Conference on Computer Vision and Pattern Recognition,2000,2:44-51.
[151] Shannon C.E. Communication Theory of Secrecy Systems[J]. Bell systemTechnology Journal.1949,28:656-715.
[152] Maurer U. A Provably-Secure Strongly-Randomized Cipher[C]. Advancesin Cryptology,2006:361-373.
[153] Gao H, Zhang Y, Liang S. A New Chaotic Algorithm for Image Encryption[J]. Chaos, Solitons&Fractals,2006,29(2):393-399.
[154] Feigenbaum M J. Quantitative Universality for A Class of NonlinearTransformations[J]. Journal of statistical physics,1978,19(1):25-52.
[155] Amin M, Faragallah O, Abd El-Latif A. A Chaotic Block Cipher Algorithmfor Image Cryptosystems[J]. Communications in Nonlinear Science andNumerical Simulation,2010,15(11):3484-3497.
[156] Wenbo Mao,现代密码学理论与实践:英文版[M].电子工业出版社,2004:118-140.