基于关键点的NAM图像特征表示及其相似性分类研究
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摘要
随着数字图像采集和传输设备的广泛使用,图像数据量激增,图像数据的自动分析理解成为海量媒体内容管理的关键问题。针对图像比较中的图像对象的自动分类识别任务,基于NAM(Non-symmetry and Anti-packing Model)方法和SIFT(Scale Invariant Feature Transform)方法,提出了一种新的灰度图像特征提取、表示和描述方法SNAM,并研究了SNAM的相似性分类检索性能,提高了图像特征提取和表示的效率和效果,实验表明能达到较为快速和准确的自动图像分类目的。
首先,快速提取图像模式中的大对象区域。视觉心理的研究成果表明在视觉初期对景物的认识由大对象开始,大对象在图像中可理解为较大面积的灰度平滑区域。为有效提取图像模式中的大对象,结合SIFT方法的关键点作为NAM图像子模式表示的起始点,采用区域生长的方式提取图像中较大面积的灰度平滑区域矩形块,从而提出了SNAM图像特征提取和表示方法。SNAM将图像模式的大对象区域表示为矩形子模式队列,提高了NAM方法对图像特征表示的一致性,具有尺度与仿射变形的不变性,在SIFT算法的基础上所增加的计算量仅为O(M),M为图像规模。
第二,提高图像子模式的特征表示效率。SIFT算法对每个关键点的灰度梯度特征采用128维的单变量向量表示,数据量较大且对同一种事物的不同对象难以聚类识别。改进SIFT算法的特征表示,将SNAM所提取的矩形子模式表示为2个16维的单变量向量,即反映子模式面积分布和布局分布特征的面积向量和布局向量,以及一个16维的双变量向量,即反映子模式位置特征的位置向量。大大降低了特征向量的维数,并且较SIFT描述值能更好地表示图像模式中图像对象的高层特征,如大对象区域的位置、布局和分布特征,实验表明能够有效地表现同一事物的不同对象在图像模式中的一致性和不同种类事物在图像模式中的区分性。SNAM图像特征描述3种向量的计算复杂度分别为O(n)、O(n)和O(n2),n为SNAM矩形子模式个数。
第三,提高相似性检索的准确度。通过研究面积特征、布局特征和位置特征的相似性距离计算和相似性检索性能,采用顺序距离、名称距离和欧氏距离对3种向量的相似性距离计算和相似性检索研究和实验表明能达到较好的准确率和召回率,在结果集较小的情况下准确度较高。相似性计算的复杂度小于O(b2),b为向量的维度,并且由于SNAM图像特征向量数据规模较小,其相似性计算的空间复杂度较低,适合快速图像检索；
最后,优化了SNAM面积向量和布局向量的SVM(Support Vector Machine)分类核函数参数。SVM核函数参数优化的实验表明在普通的点积、多项式和高斯核下,用60%的数据用于学习,40%的数据用于分类,其两类分类正确率能稳定达到75%左右；其用于学习的数据样本量较小,且多项式核函数参数在5阶以下,高斯核函数参数σ值在0.1以下；其多项式阶次较低,高斯核的非线性程度较低；进一步说明SNAM图像特征描述具有较好的快速分类识别意义。
总之,依据视觉心理原理,结合NAM方法和SIFT方法,提出了一种新的图像区域特征提取和表示方法,定义了3种数据规模较小的图像特征描述向量,提高了图像特征的提取效率和图像高层特征的表示效率,其相似性检索性能和SVM分类性能适合较为快速和准确的图像检索和图像分类识别。
With the popular use of the devices on image acquirment and data transmission image data is dramatically increasing. Image analysis and understanding become the important issue of content-based multimedia data management. With respect to object classification in image comparing, based on NAM (Non-symmetry and Anti-packing Model) and SIFT (Scale Invariant Feature Transform), a novel grey image feature extraction, representation, and description method is proposed, the similarity retrieval performance of the feature sets is studied. The new approach gives more efficient and effective on image classification by experiment.
Firstly, large scale areas in the image are extracted fastly. Research on visual psychology showed global object dominance during the beginning of visual perception. Global objects in images could be regarded as the large scale regionals with smooth gratation. In order to abstract the global object in image efficiently, keypoints of SIFT are adapted to the start points of NAM subpatterns, the gratation smooth rectangle subpatterns are extended from keypoints of SIFT by a range of grey value, as it combined SIFT and NAM, the approach is called as SNAM. The feature sets is invariant for size and radiation deformation. The additional computation cost emploied than SIFT is O(M) and M is the size of a image.
Secondly, SNAM descriptors significantly outperformed SIFT descriptors for image feature representation. In SNAM, area vector, position vector, and location vector are defined as to represent the area, positional, and local features of global objects. The area vector and the positon vector are single variable in 16 dimention, and the location vector is two variables in 16 dimention. But, in SIFT, single variable in 128 dimention was assigned to each keypoint. SNAM descriptors are more effective to represent the higher feature of images, such as the position, distribution, and location features of the large scale areas. A set of experiments demonstrated that the newly method is available for distinguishing and clustering images by the object contents. The computation cost of the three SNAM descreptors are respectively O(n), O(n), and O(n2), n is the number of subpatterns.
Thirdly, the pecision of siminarity retrieval was increased by SNAM. The siminarity retrieval performance of the area vector, position vector, and location vector are detailed. Ordered distance, nominated distance, and Eulicdean distance are adorpted during similarity measurement. Experimental results show that this approach performs better than conventional approaches in recall rate and pecision at a less retrieval results. The algorithm of similarity measure has an time complexity of O(b2), and b is the dimention of the vector. As the feature descriptor has less size of data, the algorithm has lower space complexity.
Finally, the parameters of SVM kenel classifiers using area vector and position vector as input descriptors were optimated. Experiments on area vector and position vector with SVM kenel optimation parameters denote a steady classification accuarcy at 75%, the samples for learning is 60% and others for testing. The effective kernels are inner product, polynomial, and Gaussian, for polynomianl kernels, degrees below 5, for Gaussian kernels,σvalues below 0.1.
According to the theory of visual perception, combine NAM and SIFT, A new region feature description is proposed, and 3 feature vectors are defined in 16 dimentions. Experimental resuls show the approach performs rapidity retrival in similarity measure and image classification.
首先,快速提取图像模式中的大对象区域。视觉心理的研究成果表明在视觉初期对景物的认识由大对象开始,大对象在图像中可理解为较大面积的灰度平滑区域。为有效提取图像模式中的大对象,结合SIFT方法的关键点作为NAM图像子模式表示的起始点,采用区域生长的方式提取图像中较大面积的灰度平滑区域矩形块,从而提出了SNAM图像特征提取和表示方法。SNAM将图像模式的大对象区域表示为矩形子模式队列,提高了NAM方法对图像特征表示的一致性,具有尺度与仿射变形的不变性,在SIFT算法的基础上所增加的计算量仅为O(M),M为图像规模。
第二,提高图像子模式的特征表示效率。SIFT算法对每个关键点的灰度梯度特征采用128维的单变量向量表示,数据量较大且对同一种事物的不同对象难以聚类识别。改进SIFT算法的特征表示,将SNAM所提取的矩形子模式表示为2个16维的单变量向量,即反映子模式面积分布和布局分布特征的面积向量和布局向量,以及一个16维的双变量向量,即反映子模式位置特征的位置向量。大大降低了特征向量的维数,并且较SIFT描述值能更好地表示图像模式中图像对象的高层特征,如大对象区域的位置、布局和分布特征,实验表明能够有效地表现同一事物的不同对象在图像模式中的一致性和不同种类事物在图像模式中的区分性。SNAM图像特征描述3种向量的计算复杂度分别为O(n)、O(n)和O(n2),n为SNAM矩形子模式个数。
第三,提高相似性检索的准确度。通过研究面积特征、布局特征和位置特征的相似性距离计算和相似性检索性能,采用顺序距离、名称距离和欧氏距离对3种向量的相似性距离计算和相似性检索研究和实验表明能达到较好的准确率和召回率,在结果集较小的情况下准确度较高。相似性计算的复杂度小于O(b2),b为向量的维度,并且由于SNAM图像特征向量数据规模较小,其相似性计算的空间复杂度较低,适合快速图像检索；
最后,优化了SNAM面积向量和布局向量的SVM(Support Vector Machine)分类核函数参数。SVM核函数参数优化的实验表明在普通的点积、多项式和高斯核下,用60%的数据用于学习,40%的数据用于分类,其两类分类正确率能稳定达到75%左右；其用于学习的数据样本量较小,且多项式核函数参数在5阶以下,高斯核函数参数σ值在0.1以下；其多项式阶次较低,高斯核的非线性程度较低；进一步说明SNAM图像特征描述具有较好的快速分类识别意义。
总之,依据视觉心理原理,结合NAM方法和SIFT方法,提出了一种新的图像区域特征提取和表示方法,定义了3种数据规模较小的图像特征描述向量,提高了图像特征的提取效率和图像高层特征的表示效率,其相似性检索性能和SVM分类性能适合较为快速和准确的图像检索和图像分类识别。
With the popular use of the devices on image acquirment and data transmission image data is dramatically increasing. Image analysis and understanding become the important issue of content-based multimedia data management. With respect to object classification in image comparing, based on NAM (Non-symmetry and Anti-packing Model) and SIFT (Scale Invariant Feature Transform), a novel grey image feature extraction, representation, and description method is proposed, the similarity retrieval performance of the feature sets is studied. The new approach gives more efficient and effective on image classification by experiment.
Firstly, large scale areas in the image are extracted fastly. Research on visual psychology showed global object dominance during the beginning of visual perception. Global objects in images could be regarded as the large scale regionals with smooth gratation. In order to abstract the global object in image efficiently, keypoints of SIFT are adapted to the start points of NAM subpatterns, the gratation smooth rectangle subpatterns are extended from keypoints of SIFT by a range of grey value, as it combined SIFT and NAM, the approach is called as SNAM. The feature sets is invariant for size and radiation deformation. The additional computation cost emploied than SIFT is O(M) and M is the size of a image.
Secondly, SNAM descriptors significantly outperformed SIFT descriptors for image feature representation. In SNAM, area vector, position vector, and location vector are defined as to represent the area, positional, and local features of global objects. The area vector and the positon vector are single variable in 16 dimention, and the location vector is two variables in 16 dimention. But, in SIFT, single variable in 128 dimention was assigned to each keypoint. SNAM descriptors are more effective to represent the higher feature of images, such as the position, distribution, and location features of the large scale areas. A set of experiments demonstrated that the newly method is available for distinguishing and clustering images by the object contents. The computation cost of the three SNAM descreptors are respectively O(n), O(n), and O(n2), n is the number of subpatterns.
Thirdly, the pecision of siminarity retrieval was increased by SNAM. The siminarity retrieval performance of the area vector, position vector, and location vector are detailed. Ordered distance, nominated distance, and Eulicdean distance are adorpted during similarity measurement. Experimental results show that this approach performs better than conventional approaches in recall rate and pecision at a less retrieval results. The algorithm of similarity measure has an time complexity of O(b2), and b is the dimention of the vector. As the feature descriptor has less size of data, the algorithm has lower space complexity.
Finally, the parameters of SVM kenel classifiers using area vector and position vector as input descriptors were optimated. Experiments on area vector and position vector with SVM kenel optimation parameters denote a steady classification accuarcy at 75%, the samples for learning is 60% and others for testing. The effective kernels are inner product, polynomial, and Gaussian, for polynomianl kernels, degrees below 5, for Gaussian kernels,σvalues below 0.1.
According to the theory of visual perception, combine NAM and SIFT, A new region feature description is proposed, and 3 feature vectors are defined in 16 dimentions. Experimental resuls show the approach performs rapidity retrival in similarity measure and image classification.
引文
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