视听觉情感语义相干及应用研究
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摘要
视、听、触等感觉通道是人类从外界获取信息的主要途径,它们对人类学习、记忆起着极其重要的作用,且各自分工、相互合作地实现人类信息的获取。与视觉、听觉相关的图像、音乐中蕴含着丰富的情感语义,如何提取、表示以及在多种语义信息相干下如何影响人类认知是当前计算机领域的前沿问题。
本文以探索何种情况下情感语义相干能有效促进或增强人类记忆效果为切入点展开研究,内容涉及心理学、认知科学、音乐及图像处理等学科的相关知识。具体来说,本文针对视听觉情感语义、情感语义相干以及它们对认知记忆的影响与应用等方面进行了如下探索：
1、建立了情感语义表示模型,探索了情感语义相关机理。在研究情感语义的基础上,通过形容词辨识实验建立描述情感语义的语言值集,借助自然语言处理中的语言值计算模型及模糊语义相似关系建立了情感语义表示模型；通过语义认知实验获得情感空间和情感矩阵的具体形式,定义语言值系统的语法与推理机制来研究情感语义的相干,并通过情感语义相干性实验证明此模型符合情感语义认知的心理模式,具有广泛的适用性。
2、基于特征融合建立了视觉情感语义提取算法。通过提取图像的颜色、纹理和形状特征,提出一种加权特征融合算法以更好地表达情感语义,并在图像情感语义识别映射过程中,用同一种映射算法跟单独特征的映射进行比较,证明加权特征融合算法的有效性。
3、基于SVM建立了听觉情感语义识别算法。对音乐特征的提取,采用易于解析的MIDI文件,提出一系列高层特征提取算法：基于音程统计法和SVM算法来定位主音轨,基于音调无关编码方式和字符比对的二列比较法提取主旋律。对音乐情感语义的映射,提出一种基于改进PSO算法的SVM选择性集成学习算法,通过对SVM和SVM集成学习算法比较、SVM集成学习算法和BP神经网络算法的比较,证明本算法可以有效的应用于音乐情感语义的识别中。
4、结合实际应用场景,探索了视听觉情感语义相关对认知记忆效果影响的机理。通过认知心理学实验研究不同视听觉情感语义相干对认知记忆效果的影响规律,并将其应用于煤矿救护游戏式培训系统中,以促进和提升安全救援人员的学习记忆效率。
Sense of sight, hearing and touch are main ways for human to obtain information from the outside world. They are very important for people to learn and memorize, of which the cooperation between each other with respective division of work help people acquire information. Images and music relevant with sense of sight and hearing embody rich emotional semantics. In addition, the issues of how to extract and express these emotional semantics as well as how to affect people's cognition in various semantic coherence are at the front of the computer field.
This paper, starting from exploring the circumstances under which emotional semantic coherence can effectively enhance human's memory, touches upon disciplines related to psychology, cognitive science, music and image processing, etc.. In particular, the following explorations are made in this paper on the visual and auditory emotional semantics, emotional semantic coherence and their influences on and applications in cognitive memory:
First, the emotional semantic representation model is set up and the emotional semantic mechanism is explored. Based on emotional semantics, a linguistic label set describing emotional semantics is built through adjective identification experiments and the emotional semantic representation model is established by virtue of linguistic model in natural language processing and fuzzy semantic similarity relations; the syntax and reasoning rules of linguistic label system are defined to examine the emotional semantic coherence through the specific forms of emotional space and emotional matrix obtained by semantic cognitive experiments. The emotional semantic representation model is proved to be in line with the psychological model of emotional semantic cognition and of wide applicability through the experiments of emotional semantic coherence.
Second, the visual emotional semantics extraction algorithm is set up based on feature fusion. Drawing upon the colors, texture and shapes of images, a weighted feature fusion algorithm is brought up to better express emotional semantics and compare the same mapping algorithm with mapping with separate features in the mapping process of image emotional semantic recognition in order to prove the validity of weighted feature fusion algorithm.
Third, the auditory emotional semantic recognition algorithm is established by virtue of SVM. With respect to the extraction of music characteristics, MIDI files which are easily to be analyzed are utilized and a series of high-level feature extraction algorithm are proposed as follows:locating main track through interval statistics and SVM algorithm; extracting main melody through method of comparison between two columns based on encoded system independent of tone and character comparison. As for the mapping of music emotional semantics, a SVM selective ensemble learning algorithm on the basis of improved PSO algorithm is brought up and through the comparison between SVM and SVM integration learning algorithm as well as that between SVM ensemble learning algorithm and BP neural network algorithm, the SVM selective ensemble learning algorithm is proved be able to be applied effectively into the recognition of music emotional semantics.
Fourth, the mechanism of influence of visual and auditory emotional semantics coherence on cognitive memory is probed by taking real application situation into consideration. Cognitive psychological experiments are adopted to study the rules of the influence of different visual and auditory emotional semantic coherence on cognitive memory and the rules are applied into the game-based training system for mine rescue so as to promote the learning efficiency of rescuers.
本文以探索何种情况下情感语义相干能有效促进或增强人类记忆效果为切入点展开研究,内容涉及心理学、认知科学、音乐及图像处理等学科的相关知识。具体来说,本文针对视听觉情感语义、情感语义相干以及它们对认知记忆的影响与应用等方面进行了如下探索：
1、建立了情感语义表示模型,探索了情感语义相关机理。在研究情感语义的基础上,通过形容词辨识实验建立描述情感语义的语言值集,借助自然语言处理中的语言值计算模型及模糊语义相似关系建立了情感语义表示模型；通过语义认知实验获得情感空间和情感矩阵的具体形式,定义语言值系统的语法与推理机制来研究情感语义的相干,并通过情感语义相干性实验证明此模型符合情感语义认知的心理模式,具有广泛的适用性。
2、基于特征融合建立了视觉情感语义提取算法。通过提取图像的颜色、纹理和形状特征,提出一种加权特征融合算法以更好地表达情感语义,并在图像情感语义识别映射过程中,用同一种映射算法跟单独特征的映射进行比较,证明加权特征融合算法的有效性。
3、基于SVM建立了听觉情感语义识别算法。对音乐特征的提取,采用易于解析的MIDI文件,提出一系列高层特征提取算法：基于音程统计法和SVM算法来定位主音轨,基于音调无关编码方式和字符比对的二列比较法提取主旋律。对音乐情感语义的映射,提出一种基于改进PSO算法的SVM选择性集成学习算法,通过对SVM和SVM集成学习算法比较、SVM集成学习算法和BP神经网络算法的比较,证明本算法可以有效的应用于音乐情感语义的识别中。
4、结合实际应用场景,探索了视听觉情感语义相关对认知记忆效果影响的机理。通过认知心理学实验研究不同视听觉情感语义相干对认知记忆效果的影响规律,并将其应用于煤矿救护游戏式培训系统中,以促进和提升安全救援人员的学习记忆效率。
Sense of sight, hearing and touch are main ways for human to obtain information from the outside world. They are very important for people to learn and memorize, of which the cooperation between each other with respective division of work help people acquire information. Images and music relevant with sense of sight and hearing embody rich emotional semantics. In addition, the issues of how to extract and express these emotional semantics as well as how to affect people's cognition in various semantic coherence are at the front of the computer field.
This paper, starting from exploring the circumstances under which emotional semantic coherence can effectively enhance human's memory, touches upon disciplines related to psychology, cognitive science, music and image processing, etc.. In particular, the following explorations are made in this paper on the visual and auditory emotional semantics, emotional semantic coherence and their influences on and applications in cognitive memory:
First, the emotional semantic representation model is set up and the emotional semantic mechanism is explored. Based on emotional semantics, a linguistic label set describing emotional semantics is built through adjective identification experiments and the emotional semantic representation model is established by virtue of linguistic model in natural language processing and fuzzy semantic similarity relations; the syntax and reasoning rules of linguistic label system are defined to examine the emotional semantic coherence through the specific forms of emotional space and emotional matrix obtained by semantic cognitive experiments. The emotional semantic representation model is proved to be in line with the psychological model of emotional semantic cognition and of wide applicability through the experiments of emotional semantic coherence.
Second, the visual emotional semantics extraction algorithm is set up based on feature fusion. Drawing upon the colors, texture and shapes of images, a weighted feature fusion algorithm is brought up to better express emotional semantics and compare the same mapping algorithm with mapping with separate features in the mapping process of image emotional semantic recognition in order to prove the validity of weighted feature fusion algorithm.
Third, the auditory emotional semantic recognition algorithm is established by virtue of SVM. With respect to the extraction of music characteristics, MIDI files which are easily to be analyzed are utilized and a series of high-level feature extraction algorithm are proposed as follows:locating main track through interval statistics and SVM algorithm; extracting main melody through method of comparison between two columns based on encoded system independent of tone and character comparison. As for the mapping of music emotional semantics, a SVM selective ensemble learning algorithm on the basis of improved PSO algorithm is brought up and through the comparison between SVM and SVM integration learning algorithm as well as that between SVM ensemble learning algorithm and BP neural network algorithm, the SVM selective ensemble learning algorithm is proved be able to be applied effectively into the recognition of music emotional semantics.
Fourth, the mechanism of influence of visual and auditory emotional semantics coherence on cognitive memory is probed by taking real application situation into consideration. Cognitive psychological experiments are adopted to study the rules of the influence of different visual and auditory emotional semantic coherence on cognitive memory and the rules are applied into the game-based training system for mine rescue so as to promote the learning efficiency of rescuers.
引文
[1]Guo J., Guo A. Crossmodal Interaction Between Olfactory and Visual Learning in Drosophila. Science (2005) 309:307-310.
[2]M. O. Belardinelli, etc.,Audio-visual crossmodal interactions in environmental perception:an fMRI investigation, Cognitive Processing, Volume 5, Number 3/September,2004, pp.167-174
[3]Tom M. Mitchell, AI and the Impending Revolution in Brain Sciences. AAAI Presidential Address, August,2002, Eighteenth National Conference on Artificial Intelligence.
[4]张有为.人机自然交互.国防工业出版社,2004年9月.
[5]Flickner M., H. S及, et al. Query by Image and Video content:The QBIC system. IEEE Computer, 1995.28(9):23-32.
[6]Bach J. R., C. Fuller. The virage image search engine:An open framework for image management. Proceedings SPIE Storage and Retrieval for Image and Video Databases Ⅳ,1996. San Jose, CA, USA.2670:76-87
[7]Smith J. R., S. F. Chang. Querying by color regions using the VisualSEEK content-based visual query system, in Intelligent Multimedia Information Retrieval, Maybury, M. T., Editor.1997, AAAI Press:23-42
[8]Smith J. R., S. F. Chang. VisualSEEK:A fully automated content-based image query system. Proceedings of the Fourth ACM Multimedia Conference,1996. Boston, MA:ACM Press:87-98
[9]Smith J. R.Intergrated spatial and feature image systems:Retrieval compression and analysis, in PhD thesis, Graduate School of Arts and Sciences.1997, Columbia University
[10]Shoji Thnaka, Masayuki lnoue, Michitoshi lshiwaka, etc. A method for extracting and analyzing "Kansei" fctors from Pictures, Multimedia signal Processing,1997, IEEE First workshop on, 23-25 June 1997:251-256
[11]Mackworth N H, Morandi A J, The gaze selects informative details within pictures[J], Perception & Psychophysics,1967:2547-2552
[12]Asano T. Progress on'Kansei'measurement technology and its application to visual inspection [J].INT J JPN SPREC ENG,1998,32(2):85-89.
[13]Nagamachi M. Kansei engineering [M]. Tokyo, Japan:Kaibundo publishing,1997.
[14]Picard R W. Synthetic emotion [J]. IEEE Computer Graphics and Applications,2000,20(1):52-53.
[15]王伟凝,余英林.图像的情感语义研究进展.电路与系统学报,2003,8(5)：101-109.
[16]M J Swain, D H Ballard, Color indexing, International Journal of Computer Vision,1991,7(1): 11-32.
[17]Strieker M A, Orengo M, Similarity of color images,In Proc. Of SPIE:Storage and Retrieval for Image and Video Databases Ⅲ, vol.2185:381-392.
[18]Amir A,yengar G,Smith J R,et al. Multimodal video search techniques:Late fusion of speech-based retrieval and visual content-based retrieval[J]. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings,2004,3:1048-1051
[19]Hus W, Kennedy L,Chang S F. News video story segmentation using fusion of multi-level multi-modal features in TRECVID 2003.IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings,2004,(3):645-648
[20]Huang, J. Image indexing using color correlograms. In:Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Juan:IEEE Computer Society,1997. 762-768.
[21]C Colombo, A Del Bimbo, P Pala. Semantics in Visual Information Retrieval [J]. IEEE Multimedia, 1999:6(3):38-53.
[22]Jonhannes Itten色彩艺术—色彩的主观经验与客观原理(The Art of Color)[M].上海人民美术出版社,1985.
[23]易晓编著.现代构成艺术[M].武汉大学出版社,2000.
[24]章毓晋,基于内容的视觉信息检索,科学出版社,2003：7.
[25]Haralick R M, Statistical and Structural Approaches to Texture, Proceedings of IEEE,1979,67(7): 786-804.
[26]Rui Yong, et al. Image Retrieval:Current Techniques [J]. Promising Directions and Open Issues Journal of Visual Communication and Image Representation,1999,10(3):39-62.
[27]K.T.斯托曼.情绪心理学.辽宁人民出版社.1986.
[28]孟绍兰等.当代心理学研究.北京大学出版社.1993.
[29]MIT Media Lab [EB/OL].
i. http://www..media.mit.edu/research/groups/affective-computing, Accessed 2010-05
[30]Kansei sessions, IEEE International Conference on Systems Man and Cybernetics V.6, Tokyo(Japan), October,1999.
[31]第一届中国中国情感计算及智能交互学术会议论文集合,2003.
[32]孟昭兰,人类情绪[M],上海人民出版社,1989.6.
[33]Wilhelm Wundt. Human Intelligence.[EB/OL]
i. http://www.indiana.edu/-intell/wundt.shtml, Accessed 2010-05.
[34]Tom Butler-Bowdon,50 Psychology Classics, Nicholas Brealey Publishing,2007
[35]Plutchik R, Emotion:A psychoevolutionary synthesis, New York:Harper and Row,1980
[36]Ortony A, Clore G. L, Collins A, The Cognitive Structure of Emotions [M], Cambridge, UK: Cambridge University Press,1988.
[37]Wang Yu-Jie, Wang Zhi-Liang, Wang Guo-Jiang, etc, Research on an affective model [J], Journal of Liaoning Technical University (Natural Science Edition),2006,25(4):635-637.
[38]魏哲华,基于人工心理理论的情感机器人的情感计算研究[D],北京科技大学硕士学位论文,2002.
[39]Cowie R, et al. Emotion recognition in human-computer interaction [J]. IEEE Signal Processing Magazine,2001,18(1):32-80.
[40]Ekman P, Friesen W V, Unmasking the face:A guide to recognizing emotions from facial expressions, Consulting Psychologists Press, Palo Alto, CA,1975.
[41]Yoshida K, Kato T, Yanaru T. Image Retrieval System Using Impression Words Systems, Man, and Cybernetics [A].1998 IEEE International Conference on [C].1998,3(11-14):2780-2784.
[42]Takio Kurita, Toshikazu Kato. Learning of personal visual impression for image database systems[A]. In Proceeding of the Second International Conference on Document Analysis and Recognition.547-552, Tsukuba Science City,1993,20-22.
[43]Shibata T, Kato T. Kansei image retrieval system for street landscape-discrimination and graphical parameters based on correlation of two images Systems, Man, and Cybernetics [A].1999. IEEE SMC'99 Conference Proceedings [C].1999 IEEE International Conference on,1999,6:247-252.
[44]Huang Kun Lai Mao-Sheng. Classification and extraction of image affective features [J]. Journal of Computer Applications,2008,28(3):659-662.
[45]Yoshida K, Kato T, Yanaru T. A Study of Database System with Kansei Information [A]. International Conference on System Man and Cybernetic'99 [C]. Tokyo, Japan,1999,6:253-256.
[46]Simon Haykin. Translated by Ye Shi Wei, Shi Zhong Zhi. Neural Networks:A Comprehensive Foundation,2nd Edition [M].China Machine Press,2004(in Chinese).
[47]Hayashi T, Hagiwara M. An image retrieval system to estimate impression words from images using a neural network[J].1997-IEEE International Conference on Systems, Man, and Cybernetics Computational Cybernetics and Simulation, IEEE, New York, NY,1997(1):150-155.
[48]Hayashi T, Hagiwara M. Image query by impression words-the IQI system [J]. IEEE Transactions on Consumer Electronics,1998,44(2):347-352.
[49]Jinsub Um. A Study of the Emotional Evaluation Models of Color Patterns Based on the Adaptive Fuzzy System and the Neural Network[J]. COLOR research and application,2002,27(3):208-216
[50]Wang Weining, Yu YingLin, Zhang Jianchao. Image Emotional Semantic Classification Based on Line Direction Histogram. COMPUTER ENGINGEERING,2005,31(11):7-9.
[51]Wang Shangfei et al. A Kansei Image Retrieval Based on Emotion Model. Journal of Circuits and Systems,2003,8(6):48-52.
[52]Vapnik VN. Translated by Zhang XG. The Nature of Statistical Learning Theory. TsingHua University Press,1999(in Chinese).
[53]Wang Shangfei et.al. Research of Emotion Semantic Image Annotation and Retrieval Algorithm Using Support Vector Machine. Pattern Recognition and Artificial Intelligence,2004,17(l):27-33
[54]Wan Hualin et. al..Image Semantic Classification by Using SVM. Journal of Software,2003, 14(11):1891-1899.
[55]Yu F, Chang E et al. Emotion Detection from Speech to Enrich Multimedia Content[A]. IEEE Pacific Rim Conference on Multimedia[C].2001,550-557.
[56]L.A.Zadeh. Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Syst. Man. Cybernetic, SMC9,1973:44-55.
[57]Zhao Zhenyu et. al. Introduction to Fuzzy Theory and Neural Networks and Their Application[M]. TsingHua University Press,1996.
[58]Isomoto Y, Yoshine K, Yamasaki H, lshii N. Color, Shape and Impression Keyword as Attributes of Paintings for Information Retrieval [A]. IEEE International Conference on Systems, Man, and Cybernetics[C]. Tokyo, Japan,1999,6:257-262.
[59]Chiu Chih-Yi, Yang Shi-Nine. Texture analysis and description in linguistic terms [A]. Proceedings of the Fifth Asian Conference on Computer Vision [C]. Melbourn, Australia,2002,1:205-209.
[60]Takagi H, Cho Sung-Bae, Noda T. Evaluation of an IGA-based image retrieval system using wavelet coefficients [A]. IEEE International Fuzzy Systems Conference Proceedings, FUZZ-IEEE'99 [C], South Korea,1999,3:1775-1780
[61]王次昭.音乐美学[M].北京高等教育出版社,1994.6.
[62]Picard, R.W., Affective computing:Challenges. International Joural of Human Computer studies, 2003.59(1-2):p.55-64
[63]Camurri, A., I. Lagerlo and G Volpe, Recognizing emotion加 m dance movement:Comparison of spectator recognition and automated techniques. International Journal of Human Computer Studies,2003.59(1-2):p.213-225.
[64]R. Thayer. The biopsychology of mood and arousal.1989, Oxford University Press.
[65]Hevner, K., Expression in music:a discussion of experimental studies and theories. Psychological Review,1935.42:P.186-204.
[66]Hevner, K., Experimental studies of the elements of expression in music. American Journal of Psychology,1936.48:P.246-268.
[67]Azra N. Ali, Philip H. Marsden, Affective Multi-Modal Interfaces:The Case of McGurk Effect, Proceedings of the 8th international conference on Intelligent user interfaces, Miami, Florida, USA, 2003.
[68]Jia-Yu Pan, H. J. Yang, Christos Faloutsos, Pinar Duygulu, Automatic Multimedia Cross-modal Correlation Discovery, In Prodeedings of the 10th ACM SIGKDD Conference 2004, Seattle, WA, August 22-25,2004.
[69]蔡运佳,艺术情感学.三环出版社,1989.
[70]刘华新,毛峡.图像的和谐情感特征分析.数据采集与处理,2001,16(1)：174-178.
[71]Zadel L.A著,阮达,黄崇福泽.模糊集与模糊信息粒理论.北京师范大学出版社,2007.7.
[72]Elliott C, Multi-media communication with emotion driven believable agents [C], In AAAI Spring Symposium on Believable Agents, Stanford University inPalo Alto, California,1994.
[73]刘涛.音乐情感认知模型与交互技术研究.浙江大学博十学位论文,2006.
[74]C Colombo, A Del Bimbo, P Pala. Semantics in Visual Information Retrieval [J]. IEEE Multimedia, 1999:6(3):38-53.
[75]Jonhannes Itten色彩艺术—色彩的主观经验与客观原理(The Art of Color) [M]上海人民艺术出版社,1985.
[76]Schoen, M., Gatewood, EL. The Aesthetic Attitude In Music. Psychological Monograph, 1928(39):162-183.
[77]Zadeh L A.Fuzzy logic= computing with words[J]. IEEE Transactions on Fuzzy Systems,1996,4(2):103-111.
[78]Tang Yongchuan,Zheng Jiacheng. Linguistic modeling based on semantic similarity relation among linguistic labels[J]. Fuzzy Sets and Systems,2006,157(12):1662-1673.
[79]Lawry J. A framework for linguistic modeling. Artificial Intelligence,2004(155):1-39.
[80]孙守迁,王鑫等.音乐情感的语言值计算模型研究.北京邮电大学学报,2006.11
[81]M. L. Swain, D. H. Ballard. Color Indexing. International Journal of Computer Vision,1991. 7(1):11-32.
[82]Huang J., S. R. kumar, et al., Image indexing using color correlogram. IEEE Conference of Computer Vision Pattern Recognition(CVPR 97),1997, San. Juan, Puerto Rico:762-768.
[83]M Stricker and M Orengo. Similarity of color images.SPIE Storage and Retrieval for Image and Video Databases Ⅲ,Feb 1995,Vol 2185:381-392
[84]John R Smith, Shih-Fu Chang. Tools and techniques for color image retrieval. In Proc of SPIE: Storage and Retrieval for Image and Video Database Vol 2670,1995
[85]Fuh, C. S., S. W. Cho, et al., Hierarchical Color Image Region Segmentation for Content-based Image Retrieval System. IEEE Transaction on Image Processing,2000.9(1):156-163.
[86]H Tamura,S Mori, T Yamawaki. Texture features corresponding to visual perception. IEEE Trans On Systems,Man and Cybernetics,June 1978,Vol Smc-8,No6.
[87]M H Gross, R Koch, Li Lippert and A Dreger. Multiscale image texture analysis in wavelet spaces. In Proc IEEE Int Conf on Image Proc,1994
[88]K S Thyagarajan,Tom Nguyen and Charles Persons, A maximum likelihood approach to texture classification using wavelet transform, In Proc IEEE Int Conf on Image Proc,1994.
[89]Pirrone, R., M. L. Cascia. Texture Classification for Content-based Image Retrieval. International Conference on Image Analysis and Processing(ICIAP),2001. Palermo, Italy:398-403.
[90]Loncaric S., A survey of shape analysis techniques. Pattern Recognition,1998,31(8):983-1001.
[91]Hu M. K. Visual pattern recognition by moment invariants. IEEE Transactions Information Theory. 1962,8(2):179-187.
[92]A Pentland and R W Picard and S Sclaroff. Photobook:Content-based manipulation of image databases. Int J Comput Vis,1996,18(3),233-254
[93]Esther M Arkin,L Chew,D Huttenlocher,K Kedem and J Mitchell. An efficienly computable metric for comparing polygonal shapes. IEEE Trans Part Recog And Mach Intell, March 1991,13(3)
[94]Gene C H Chuang and C C Jay Kuo. Wavelet descriptor of planar curves:Theory and applications. IEEE Trans Image Proc, tan 1996,5(1),56-70
[95]Eakins J. P., M. E. Graham. Content based image retrieval.1999,A report to the JISC Technology Application Programme.
[96]王惠锋,孙止兴等.语义图像检索研究进展.计算机研究与发展,2002,39(5)：513-523.
[97]Jaimes A., S. F. Chang. Model-based classification of visual information for content-based retrieval. SP1E Conference on Storage and Retrieval for Image and Video Databases VII,1999, San Jose, CA. 3656:402-414.
[98]Hong D., J. K. Wu, et al. Refining Image Retrieval Based on Content Driven Methods. IS&SPIE 1 lth Symposium on Electronic Imaging,1999. San Jose, CA, USA:581-592.
[99]朱兴全,张宏江iFind一个结合语义和视觉特征的图像相关反馈检索系统.计算机学报,2002,25(7)：681-688.
[100]Yin X., M. Li, et al.. Semantic Image Clustering Using Relevance Feedback. IEEE International Symposium on Circuits and Systems (ISCAS'03) 2003.2:904-907.
[101]He X., O. King, et al.. Learning a semantic space from user's relevance feedback for image retrieval. IEEE Transactions on Circuits and Systems for Video Technology,2003.13(l):39-48.
[102]Shen H. T., B. C. Ooi, et al.. Giving meanings to www Images. Proceedings of the 8th ACM international Conference on Multimedia (ACM MM),2000, Los Angeles, California:39-47.
[103]Srihari R. K.., Z. Zhang, et al.. Intelligent indexing and semantic retrieval of multimodal documents. Information Retrieval,2000,2(2):245-275.
[104]张华,张淼等著.基于外部信息源的WWW图像语义提取研究.计算机科学,2006,33(4)：211-214.
[105]王上飞,陈恩红等著.基于支持向量机的图像情感语义注释和检索算法的研究。模式识别与人工智能,2004,17(1)：27-33.
[106]Allan M. Collins, M.R. Quillian. Retrieval time from semantic memory. Journal of verbal learning and verbal behavior 8 (2):240-247.
[107]Allan M. Collins, M. Ross Quillian.Does category size affect categorization time? Journal of verbal learning and verbal behavior 9 (4):432-438.
[108]Herbert A. Simon, Steven Rosenberg. Modeling semantic memory:Effects of presenting semantic information in different modalities. Cognitive psychology,1977, vol.9(3):293-325.
[109]Li Q., H. Hu, et al.. Semantic feature extraction using genetic programming in image retrieval. Proceeding of the 17th International Conference on Pattern Recognition,2004. Beijing, China,l: 648-651.
[110]Yongchuan Tang,Jiacheng Zheng. Linguistic modelling based on semantics similaity relation among linguistic labels. Fuzzy sets and systems 157(2006) 1662-1673.
[111]金毅.中国民族音乐数据库基于旋律的检索技术研究,上海交通大学学位论文,2003,P.48.
[112]李重光.基本乐理简明教程,1990,人民音乐出版社.
[113]刘涛.音乐情感认知模型与交互技术研究.浙江大学博十学位论文,2006.
[114]冯雅中,庄越挺,潘云鹤.一种启发式的用哼唱检索音乐的层次化方法.计算机研究与发展,2004.41(2)：333-339.
[115]Kosugi,N., Y. Nishihara, T. Sakata. A practical query-by humming system for a large music database. The ACM Multimedia.2000. Los Angeles, CA.
[116]Tseng, Y. H., Content-based retrieval for music collections. SIGIRForum(ACM Special Interest Group on Information Retrieval),1999:p.176-182.
[117]Shih, H. H., S. S. Narayanan, C.C.J. Kuo. Comparison of dictionary-based approaches to automatic repeating melody extraction.2002. San Jose, CA, United States:The International Society for Optical Engineering.
[118]Yu-lung, L., Y. Ho-cheng, F. Mei-chin. FastPET:A Fast Non-trivial Repeating Pattern Extracting Technique for Music Data.2001 National Computer Symposium Multimedia, Computer Graphics, and Image Processing. Taipei.
[119]杜亚雄,中国传统乐理教程.2004,上海：上海音乐出版社.
[120]Lawry. J., Modelling and Reasoning with Vague Concepts.2006:Springer.
[121]舒华,张亚旭.心理学研究方法实验设计与数据分析.2010,北京：人民教育出版社.
[122]Chiu Chih-Yi, Yang Shi-Nine. Texture analysis and description in Linguistic terms [A]. Proceedings of the Fifth Asian Conference on Computer Vision [C]. Melbourn, Australia,2002.1:205-209.
[123]王伟凝.基于情感语义的图像特征提取、检索与分类研究.2005,华南理工大学博士学位论文。
[124]Zengrong Liu, Xueli Yu. Research on Linguistic Computing Model for Image Emotion Semantic. 2011 International Conference on Transportation and Mechanical & Electrical Engineering. IEEE Systems, Man & Cybernetics Society,2011(19):3490-3493.
[125]T. G. Dietterich. Ensemble Methods in Machine Learning. In Multiple Classier System, Cagliari, Italy,2000.
[126]G. Valentini, F. Masulli. "Ensembles of Learning Machines" in Neural Nets WIRN Vietri-02, Series Lecture Notes in Computer Science, M. Marinaro and R. Tagliaferri, Eds.:Springer-Verlag, Heidelberg (Germany),2002.
[127]Schapire R. E.. The Strength of weak learnability [J]. Machine Learning,1990,5(2):197-227.
[128]Breiman L. Bagging predicators [J]. Machine Learning,1996,24(2):123-140.
[129]王珏,周志华,周傲英.机器学习应用[M].北京：清华大学出版社,2006：170-188.
[130]Z.-H. Zhou. Ensemble Methods:Foundations and Algorithms, Boca Raton, FL:Chapman & Hall/CRC,2012.
[131]Z.-H. Zhou, J.-X. Wu, Y. Jiang, and S.-F. Chen. Genetic algorithm based selective neural network ensemble. In:Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI'01), Seattle, WA,2001, vol.2, pp.797-802.
[132]Kennedy J, Eberhart R C. Particle swarm optimization [C]. Proc of the IEEE International Conference on Neural Networks. Piscataway, NJ:IEEE Service Center,1995:1942-1948.
[133]李丽,牛奔.粒子群优化算法.北京：冶金工业出版社,2009.
[134]L. Wang, Q. Kang, H. Xiao, Q. Wu. A Modified adaptive particle swarm optimization algorithm [C]. New York:Proceedings of the IEEE International Conference on Industrial Technology, 2005:209-214.
[135]姚合宝等。光场的时间相干性和空间相干性分析。西安建筑科技大学学报,2000,32(4)：401-403.
[136]刘壮虎.相干逻辑的领域语义学。1995年逻辑研究专辑。
[137]欧阳淑兰.情绪对个体外显和内隐记忆的影响研究综述.绥化学院学报,2007,27(6)：181-183.
[138]齐平,梁承谋.诱发正负情绪对外显记忆和内隐记忆的影响.西南师范大学学报,2003,28(1)：143-147。
[139]杨强,马森.基于因子分析和概率神经网络的情感语义提取.四川理工学院学报,2009,22(4)：62-64.
[2]M. O. Belardinelli, etc.,Audio-visual crossmodal interactions in environmental perception:an fMRI investigation, Cognitive Processing, Volume 5, Number 3/September,2004, pp.167-174
[3]Tom M. Mitchell, AI and the Impending Revolution in Brain Sciences. AAAI Presidential Address, August,2002, Eighteenth National Conference on Artificial Intelligence.
[4]张有为.人机自然交互.国防工业出版社,2004年9月.
[5]Flickner M., H. S及, et al. Query by Image and Video content:The QBIC system. IEEE Computer, 1995.28(9):23-32.
[6]Bach J. R., C. Fuller. The virage image search engine:An open framework for image management. Proceedings SPIE Storage and Retrieval for Image and Video Databases Ⅳ,1996. San Jose, CA, USA.2670:76-87
[7]Smith J. R., S. F. Chang. Querying by color regions using the VisualSEEK content-based visual query system, in Intelligent Multimedia Information Retrieval, Maybury, M. T., Editor.1997, AAAI Press:23-42
[8]Smith J. R., S. F. Chang. VisualSEEK:A fully automated content-based image query system. Proceedings of the Fourth ACM Multimedia Conference,1996. Boston, MA:ACM Press:87-98
[9]Smith J. R.Intergrated spatial and feature image systems:Retrieval compression and analysis, in PhD thesis, Graduate School of Arts and Sciences.1997, Columbia University
[10]Shoji Thnaka, Masayuki lnoue, Michitoshi lshiwaka, etc. A method for extracting and analyzing "Kansei" fctors from Pictures, Multimedia signal Processing,1997, IEEE First workshop on, 23-25 June 1997:251-256
[11]Mackworth N H, Morandi A J, The gaze selects informative details within pictures[J], Perception & Psychophysics,1967:2547-2552
[12]Asano T. Progress on'Kansei'measurement technology and its application to visual inspection [J].INT J JPN SPREC ENG,1998,32(2):85-89.
[13]Nagamachi M. Kansei engineering [M]. Tokyo, Japan:Kaibundo publishing,1997.
[14]Picard R W. Synthetic emotion [J]. IEEE Computer Graphics and Applications,2000,20(1):52-53.
[15]王伟凝,余英林.图像的情感语义研究进展.电路与系统学报,2003,8(5)：101-109.
[16]M J Swain, D H Ballard, Color indexing, International Journal of Computer Vision,1991,7(1): 11-32.
[17]Strieker M A, Orengo M, Similarity of color images,In Proc. Of SPIE:Storage and Retrieval for Image and Video Databases Ⅲ, vol.2185:381-392.
[18]Amir A,yengar G,Smith J R,et al. Multimodal video search techniques:Late fusion of speech-based retrieval and visual content-based retrieval[J]. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings,2004,3:1048-1051
[19]Hus W, Kennedy L,Chang S F. News video story segmentation using fusion of multi-level multi-modal features in TRECVID 2003.IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings,2004,(3):645-648
[20]Huang, J. Image indexing using color correlograms. In:Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Juan:IEEE Computer Society,1997. 762-768.
[21]C Colombo, A Del Bimbo, P Pala. Semantics in Visual Information Retrieval [J]. IEEE Multimedia, 1999:6(3):38-53.
[22]Jonhannes Itten色彩艺术—色彩的主观经验与客观原理(The Art of Color)[M].上海人民美术出版社,1985.
[23]易晓编著.现代构成艺术[M].武汉大学出版社,2000.
[24]章毓晋,基于内容的视觉信息检索,科学出版社,2003：7.
[25]Haralick R M, Statistical and Structural Approaches to Texture, Proceedings of IEEE,1979,67(7): 786-804.
[26]Rui Yong, et al. Image Retrieval:Current Techniques [J]. Promising Directions and Open Issues Journal of Visual Communication and Image Representation,1999,10(3):39-62.
[27]K.T.斯托曼.情绪心理学.辽宁人民出版社.1986.
[28]孟绍兰等.当代心理学研究.北京大学出版社.1993.
[29]MIT Media Lab [EB/OL].
i. http://www..media.mit.edu/research/groups/affective-computing, Accessed 2010-05
[30]Kansei sessions, IEEE International Conference on Systems Man and Cybernetics V.6, Tokyo(Japan), October,1999.
[31]第一届中国中国情感计算及智能交互学术会议论文集合,2003.
[32]孟昭兰,人类情绪[M],上海人民出版社,1989.6.
[33]Wilhelm Wundt. Human Intelligence.[EB/OL]
i. http://www.indiana.edu/-intell/wundt.shtml, Accessed 2010-05.
[34]Tom Butler-Bowdon,50 Psychology Classics, Nicholas Brealey Publishing,2007
[35]Plutchik R, Emotion:A psychoevolutionary synthesis, New York:Harper and Row,1980
[36]Ortony A, Clore G. L, Collins A, The Cognitive Structure of Emotions [M], Cambridge, UK: Cambridge University Press,1988.
[37]Wang Yu-Jie, Wang Zhi-Liang, Wang Guo-Jiang, etc, Research on an affective model [J], Journal of Liaoning Technical University (Natural Science Edition),2006,25(4):635-637.
[38]魏哲华,基于人工心理理论的情感机器人的情感计算研究[D],北京科技大学硕士学位论文,2002.
[39]Cowie R, et al. Emotion recognition in human-computer interaction [J]. IEEE Signal Processing Magazine,2001,18(1):32-80.
[40]Ekman P, Friesen W V, Unmasking the face:A guide to recognizing emotions from facial expressions, Consulting Psychologists Press, Palo Alto, CA,1975.
[41]Yoshida K, Kato T, Yanaru T. Image Retrieval System Using Impression Words Systems, Man, and Cybernetics [A].1998 IEEE International Conference on [C].1998,3(11-14):2780-2784.
[42]Takio Kurita, Toshikazu Kato. Learning of personal visual impression for image database systems[A]. In Proceeding of the Second International Conference on Document Analysis and Recognition.547-552, Tsukuba Science City,1993,20-22.
[43]Shibata T, Kato T. Kansei image retrieval system for street landscape-discrimination and graphical parameters based on correlation of two images Systems, Man, and Cybernetics [A].1999. IEEE SMC'99 Conference Proceedings [C].1999 IEEE International Conference on,1999,6:247-252.
[44]Huang Kun Lai Mao-Sheng. Classification and extraction of image affective features [J]. Journal of Computer Applications,2008,28(3):659-662.
[45]Yoshida K, Kato T, Yanaru T. A Study of Database System with Kansei Information [A]. International Conference on System Man and Cybernetic'99 [C]. Tokyo, Japan,1999,6:253-256.
[46]Simon Haykin. Translated by Ye Shi Wei, Shi Zhong Zhi. Neural Networks:A Comprehensive Foundation,2nd Edition [M].China Machine Press,2004(in Chinese).
[47]Hayashi T, Hagiwara M. An image retrieval system to estimate impression words from images using a neural network[J].1997-IEEE International Conference on Systems, Man, and Cybernetics Computational Cybernetics and Simulation, IEEE, New York, NY,1997(1):150-155.
[48]Hayashi T, Hagiwara M. Image query by impression words-the IQI system [J]. IEEE Transactions on Consumer Electronics,1998,44(2):347-352.
[49]Jinsub Um. A Study of the Emotional Evaluation Models of Color Patterns Based on the Adaptive Fuzzy System and the Neural Network[J]. COLOR research and application,2002,27(3):208-216
[50]Wang Weining, Yu YingLin, Zhang Jianchao. Image Emotional Semantic Classification Based on Line Direction Histogram. COMPUTER ENGINGEERING,2005,31(11):7-9.
[51]Wang Shangfei et al. A Kansei Image Retrieval Based on Emotion Model. Journal of Circuits and Systems,2003,8(6):48-52.
[52]Vapnik VN. Translated by Zhang XG. The Nature of Statistical Learning Theory. TsingHua University Press,1999(in Chinese).
[53]Wang Shangfei et.al. Research of Emotion Semantic Image Annotation and Retrieval Algorithm Using Support Vector Machine. Pattern Recognition and Artificial Intelligence,2004,17(l):27-33
[54]Wan Hualin et. al..Image Semantic Classification by Using SVM. Journal of Software,2003, 14(11):1891-1899.
[55]Yu F, Chang E et al. Emotion Detection from Speech to Enrich Multimedia Content[A]. IEEE Pacific Rim Conference on Multimedia[C].2001,550-557.
[56]L.A.Zadeh. Outline of a new approach to the analysis of complex systems and decision processes, IEEE Trans. Syst. Man. Cybernetic, SMC9,1973:44-55.
[57]Zhao Zhenyu et. al. Introduction to Fuzzy Theory and Neural Networks and Their Application[M]. TsingHua University Press,1996.
[58]Isomoto Y, Yoshine K, Yamasaki H, lshii N. Color, Shape and Impression Keyword as Attributes of Paintings for Information Retrieval [A]. IEEE International Conference on Systems, Man, and Cybernetics[C]. Tokyo, Japan,1999,6:257-262.
[59]Chiu Chih-Yi, Yang Shi-Nine. Texture analysis and description in linguistic terms [A]. Proceedings of the Fifth Asian Conference on Computer Vision [C]. Melbourn, Australia,2002,1:205-209.
[60]Takagi H, Cho Sung-Bae, Noda T. Evaluation of an IGA-based image retrieval system using wavelet coefficients [A]. IEEE International Fuzzy Systems Conference Proceedings, FUZZ-IEEE'99 [C], South Korea,1999,3:1775-1780
[61]王次昭.音乐美学[M].北京高等教育出版社,1994.6.
[62]Picard, R.W., Affective computing:Challenges. International Joural of Human Computer studies, 2003.59(1-2):p.55-64
[63]Camurri, A., I. Lagerlo and G Volpe, Recognizing emotion加 m dance movement:Comparison of spectator recognition and automated techniques. International Journal of Human Computer Studies,2003.59(1-2):p.213-225.
[64]R. Thayer. The biopsychology of mood and arousal.1989, Oxford University Press.
[65]Hevner, K., Expression in music:a discussion of experimental studies and theories. Psychological Review,1935.42:P.186-204.
[66]Hevner, K., Experimental studies of the elements of expression in music. American Journal of Psychology,1936.48:P.246-268.
[67]Azra N. Ali, Philip H. Marsden, Affective Multi-Modal Interfaces:The Case of McGurk Effect, Proceedings of the 8th international conference on Intelligent user interfaces, Miami, Florida, USA, 2003.
[68]Jia-Yu Pan, H. J. Yang, Christos Faloutsos, Pinar Duygulu, Automatic Multimedia Cross-modal Correlation Discovery, In Prodeedings of the 10th ACM SIGKDD Conference 2004, Seattle, WA, August 22-25,2004.
[69]蔡运佳,艺术情感学.三环出版社,1989.
[70]刘华新,毛峡.图像的和谐情感特征分析.数据采集与处理,2001,16(1)：174-178.
[71]Zadel L.A著,阮达,黄崇福泽.模糊集与模糊信息粒理论.北京师范大学出版社,2007.7.
[72]Elliott C, Multi-media communication with emotion driven believable agents [C], In AAAI Spring Symposium on Believable Agents, Stanford University inPalo Alto, California,1994.
[73]刘涛.音乐情感认知模型与交互技术研究.浙江大学博十学位论文,2006.
[74]C Colombo, A Del Bimbo, P Pala. Semantics in Visual Information Retrieval [J]. IEEE Multimedia, 1999:6(3):38-53.
[75]Jonhannes Itten色彩艺术—色彩的主观经验与客观原理(The Art of Color) [M]上海人民艺术出版社,1985.
[76]Schoen, M., Gatewood, EL. The Aesthetic Attitude In Music. Psychological Monograph, 1928(39):162-183.
[77]Zadeh L A.Fuzzy logic= computing with words[J]. IEEE Transactions on Fuzzy Systems,1996,4(2):103-111.
[78]Tang Yongchuan,Zheng Jiacheng. Linguistic modeling based on semantic similarity relation among linguistic labels[J]. Fuzzy Sets and Systems,2006,157(12):1662-1673.
[79]Lawry J. A framework for linguistic modeling. Artificial Intelligence,2004(155):1-39.
[80]孙守迁,王鑫等.音乐情感的语言值计算模型研究.北京邮电大学学报,2006.11
[81]M. L. Swain, D. H. Ballard. Color Indexing. International Journal of Computer Vision,1991. 7(1):11-32.
[82]Huang J., S. R. kumar, et al., Image indexing using color correlogram. IEEE Conference of Computer Vision Pattern Recognition(CVPR 97),1997, San. Juan, Puerto Rico:762-768.
[83]M Stricker and M Orengo. Similarity of color images.SPIE Storage and Retrieval for Image and Video Databases Ⅲ,Feb 1995,Vol 2185:381-392
[84]John R Smith, Shih-Fu Chang. Tools and techniques for color image retrieval. In Proc of SPIE: Storage and Retrieval for Image and Video Database Vol 2670,1995
[85]Fuh, C. S., S. W. Cho, et al., Hierarchical Color Image Region Segmentation for Content-based Image Retrieval System. IEEE Transaction on Image Processing,2000.9(1):156-163.
[86]H Tamura,S Mori, T Yamawaki. Texture features corresponding to visual perception. IEEE Trans On Systems,Man and Cybernetics,June 1978,Vol Smc-8,No6.
[87]M H Gross, R Koch, Li Lippert and A Dreger. Multiscale image texture analysis in wavelet spaces. In Proc IEEE Int Conf on Image Proc,1994
[88]K S Thyagarajan,Tom Nguyen and Charles Persons, A maximum likelihood approach to texture classification using wavelet transform, In Proc IEEE Int Conf on Image Proc,1994.
[89]Pirrone, R., M. L. Cascia. Texture Classification for Content-based Image Retrieval. International Conference on Image Analysis and Processing(ICIAP),2001. Palermo, Italy:398-403.
[90]Loncaric S., A survey of shape analysis techniques. Pattern Recognition,1998,31(8):983-1001.
[91]Hu M. K. Visual pattern recognition by moment invariants. IEEE Transactions Information Theory. 1962,8(2):179-187.
[92]A Pentland and R W Picard and S Sclaroff. Photobook:Content-based manipulation of image databases. Int J Comput Vis,1996,18(3),233-254
[93]Esther M Arkin,L Chew,D Huttenlocher,K Kedem and J Mitchell. An efficienly computable metric for comparing polygonal shapes. IEEE Trans Part Recog And Mach Intell, March 1991,13(3)
[94]Gene C H Chuang and C C Jay Kuo. Wavelet descriptor of planar curves:Theory and applications. IEEE Trans Image Proc, tan 1996,5(1),56-70
[95]Eakins J. P., M. E. Graham. Content based image retrieval.1999,A report to the JISC Technology Application Programme.
[96]王惠锋,孙止兴等.语义图像检索研究进展.计算机研究与发展,2002,39(5)：513-523.
[97]Jaimes A., S. F. Chang. Model-based classification of visual information for content-based retrieval. SP1E Conference on Storage and Retrieval for Image and Video Databases VII,1999, San Jose, CA. 3656:402-414.
[98]Hong D., J. K. Wu, et al. Refining Image Retrieval Based on Content Driven Methods. IS&SPIE 1 lth Symposium on Electronic Imaging,1999. San Jose, CA, USA:581-592.
[99]朱兴全,张宏江iFind一个结合语义和视觉特征的图像相关反馈检索系统.计算机学报,2002,25(7)：681-688.
[100]Yin X., M. Li, et al.. Semantic Image Clustering Using Relevance Feedback. IEEE International Symposium on Circuits and Systems (ISCAS'03) 2003.2:904-907.
[101]He X., O. King, et al.. Learning a semantic space from user's relevance feedback for image retrieval. IEEE Transactions on Circuits and Systems for Video Technology,2003.13(l):39-48.
[102]Shen H. T., B. C. Ooi, et al.. Giving meanings to www Images. Proceedings of the 8th ACM international Conference on Multimedia (ACM MM),2000, Los Angeles, California:39-47.
[103]Srihari R. K.., Z. Zhang, et al.. Intelligent indexing and semantic retrieval of multimodal documents. Information Retrieval,2000,2(2):245-275.
[104]张华,张淼等著.基于外部信息源的WWW图像语义提取研究.计算机科学,2006,33(4)：211-214.
[105]王上飞,陈恩红等著.基于支持向量机的图像情感语义注释和检索算法的研究。模式识别与人工智能,2004,17(1)：27-33.
[106]Allan M. Collins, M.R. Quillian. Retrieval time from semantic memory. Journal of verbal learning and verbal behavior 8 (2):240-247.
[107]Allan M. Collins, M. Ross Quillian.Does category size affect categorization time? Journal of verbal learning and verbal behavior 9 (4):432-438.
[108]Herbert A. Simon, Steven Rosenberg. Modeling semantic memory:Effects of presenting semantic information in different modalities. Cognitive psychology,1977, vol.9(3):293-325.
[109]Li Q., H. Hu, et al.. Semantic feature extraction using genetic programming in image retrieval. Proceeding of the 17th International Conference on Pattern Recognition,2004. Beijing, China,l: 648-651.
[110]Yongchuan Tang,Jiacheng Zheng. Linguistic modelling based on semantics similaity relation among linguistic labels. Fuzzy sets and systems 157(2006) 1662-1673.
[111]金毅.中国民族音乐数据库基于旋律的检索技术研究,上海交通大学学位论文,2003,P.48.
[112]李重光.基本乐理简明教程,1990,人民音乐出版社.
[113]刘涛.音乐情感认知模型与交互技术研究.浙江大学博十学位论文,2006.
[114]冯雅中,庄越挺,潘云鹤.一种启发式的用哼唱检索音乐的层次化方法.计算机研究与发展,2004.41(2)：333-339.
[115]Kosugi,N., Y. Nishihara, T. Sakata. A practical query-by humming system for a large music database. The ACM Multimedia.2000. Los Angeles, CA.
[116]Tseng, Y. H., Content-based retrieval for music collections. SIGIRForum(ACM Special Interest Group on Information Retrieval),1999:p.176-182.
[117]Shih, H. H., S. S. Narayanan, C.C.J. Kuo. Comparison of dictionary-based approaches to automatic repeating melody extraction.2002. San Jose, CA, United States:The International Society for Optical Engineering.
[118]Yu-lung, L., Y. Ho-cheng, F. Mei-chin. FastPET:A Fast Non-trivial Repeating Pattern Extracting Technique for Music Data.2001 National Computer Symposium Multimedia, Computer Graphics, and Image Processing. Taipei.
[119]杜亚雄,中国传统乐理教程.2004,上海：上海音乐出版社.
[120]Lawry. J., Modelling and Reasoning with Vague Concepts.2006:Springer.
[121]舒华,张亚旭.心理学研究方法实验设计与数据分析.2010,北京：人民教育出版社.
[122]Chiu Chih-Yi, Yang Shi-Nine. Texture analysis and description in Linguistic terms [A]. Proceedings of the Fifth Asian Conference on Computer Vision [C]. Melbourn, Australia,2002.1:205-209.
[123]王伟凝.基于情感语义的图像特征提取、检索与分类研究.2005,华南理工大学博士学位论文。
[124]Zengrong Liu, Xueli Yu. Research on Linguistic Computing Model for Image Emotion Semantic. 2011 International Conference on Transportation and Mechanical & Electrical Engineering. IEEE Systems, Man & Cybernetics Society,2011(19):3490-3493.
[125]T. G. Dietterich. Ensemble Methods in Machine Learning. In Multiple Classier System, Cagliari, Italy,2000.
[126]G. Valentini, F. Masulli. "Ensembles of Learning Machines" in Neural Nets WIRN Vietri-02, Series Lecture Notes in Computer Science, M. Marinaro and R. Tagliaferri, Eds.:Springer-Verlag, Heidelberg (Germany),2002.
[127]Schapire R. E.. The Strength of weak learnability [J]. Machine Learning,1990,5(2):197-227.
[128]Breiman L. Bagging predicators [J]. Machine Learning,1996,24(2):123-140.
[129]王珏,周志华,周傲英.机器学习应用[M].北京：清华大学出版社,2006：170-188.
[130]Z.-H. Zhou. Ensemble Methods:Foundations and Algorithms, Boca Raton, FL:Chapman & Hall/CRC,2012.
[131]Z.-H. Zhou, J.-X. Wu, Y. Jiang, and S.-F. Chen. Genetic algorithm based selective neural network ensemble. In:Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI'01), Seattle, WA,2001, vol.2, pp.797-802.
[132]Kennedy J, Eberhart R C. Particle swarm optimization [C]. Proc of the IEEE International Conference on Neural Networks. Piscataway, NJ:IEEE Service Center,1995:1942-1948.
[133]李丽,牛奔.粒子群优化算法.北京：冶金工业出版社,2009.
[134]L. Wang, Q. Kang, H. Xiao, Q. Wu. A Modified adaptive particle swarm optimization algorithm [C]. New York:Proceedings of the IEEE International Conference on Industrial Technology, 2005:209-214.
[135]姚合宝等。光场的时间相干性和空间相干性分析。西安建筑科技大学学报,2000,32(4)：401-403.
[136]刘壮虎.相干逻辑的领域语义学。1995年逻辑研究专辑。
[137]欧阳淑兰.情绪对个体外显和内隐记忆的影响研究综述.绥化学院学报,2007,27(6)：181-183.
[138]齐平,梁承谋.诱发正负情绪对外显记忆和内隐记忆的影响.西南师范大学学报,2003,28(1)：143-147。
[139]杨强,马森.基于因子分析和概率神经网络的情感语义提取.四川理工学院学报,2009,22(4)：62-64.
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