Dictionary Learning Guided by Minimum Class Variance Support Vector

doi:10.19678/j.issn.1000-3428.0053661

Abstract

Abstract: Existing Support Vector Guided Dictionary Learning(SVGDL) algorithm based on the principle of large-margin classification.When establishing decision-making hyperplanes,the algorithms consider only the boundary conditions of each class of encoding vectors,but ignore data distribution information,which limits the generalization ability of the model.To address the problem,this paper proposes a Minimum Class Variance Support Vector Guided Dictionary Learning(MCVGDL) algorithm.First,MCVGDL takes the Minimum Class Variance Support Vector Machine(MCVSVM),which combines Fisher linear discriminant analysis and the large margin classification principle of Support Vector Machine(SVM),as discriminant term.Second,during alternate optimization of model classifiers,MCVGDL comprehensively takes the distribution information of encoding vectors into account,to guarantee the overall consistency of encoding vectors of similar samples and reduce the coupling degree of corresponding components between vectors and modifies SVM classification vectors.So,the discriminant information of encoding vectors can be fully mined to better guide dictionary learning,improving the classification performance.Experimental results on face,object,and handwritten digit recognition datasets show that in terms of the recognition rate and atomic robustness,the proposed algorithm outperforms classical dictionary learning algorithms,including K Singular Value Decomposition(KSVD) and Local Constrained and Label Embedding Dictionary Learning(LCLE-DL),etc.

Key words: dictionary learning, Collaborative Representation(CR), encoding vector, minimum class variance support vector, digital image recognition

摘要： 支持向量引导的字典学习算法依据大间隔分类原则，仅考虑每类编码向量边界条件建立决策超平面，未利用数据的分布信息，在一定程度上限制了模型的泛化能力。为解决该问题，提出最小类内方差支持向量引导的字典学习算法。将融合Fisher线性鉴别分析和支持向量机大间隔分类准则的最小类内方差支持向量机作为鉴别条件，在模型分类器的交替优化过程中，充分考虑编码向量的分布信息，保障同类编码向量总体一致的同时降低向量间的耦合度并修正分类矢量，从而挖掘编码向量鉴别信息，使其更好地引导字典学习以提高算法分类性能。在人脸、物体和手写数字识别数据集上的实验结果表明，在大部分样本和原子数量条件下，该算法的识别率和原子鲁棒性均优于K奇异值分解、局部特征和类标嵌入约束等经典字典学习算法。

关键词: 字典学习, 协作表达, 编码向量, 最小类内方差支持向量, 数字图像识别

CLC Number:

TP391.4

WANG Xiaoming, XU Tao, RAN Biao. Dictionary Learning Guided by Minimum Class Variance Support Vector[J]. Computer Engineering, 2020, 46(4): 60-69.

王晓明, 徐涛, 冉彪. 最小类内方差支持向量引导的字典学习[J]. 计算机工程, 2020, 46(4): 60-69.

/ Recommend / Download Citations

URL: https://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0053661

https://www.ecice06.com/EN/Y2020/V46/I4/60

Figures/Tables 12

References 36

[1]	LIU Lihui,XU Jun,GONG Lei.Breast image classification based on multi-feature joint supervised dictionary learning[J].Computer Engineering,2018,44(3):245-250,258.(in Chinese)刘利卉,徐军,龚磊.基于多特征联合监督字典学习的乳腺图像分类[J].计算机工程,2018,44(3):245-250,258.
[2]	ELAD M,AHAQRON M.Image denoising via sparse and redundant representations over learned dictionaries[J].IEEE Transactions on Image Processing,2006,15(12):3736-3745.
[3]	WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2009,31(2):210-227.
[4]	WANG Liutao,HUANG Miao,WANG Jianxi,et al.Single frame super resolution algorithm of intersected-resolution adaptive dictionary learning[J].Computer Engineering,2016,42(8):206-210,219.(in Chinese)王刘涛,黄淼,王建玺,等.交叉分辨率自适应字典学习的单帧超分辨率算法[J].计算机工程,2016,42(8):206-210,219.
[5]	WANG Keping,YANG Zanya,EN De.Image compression method based on sparse representation of classified redundant dictionary[J].Computer Engineering,2017,43(9):287-293.(in Chinese)王科平,杨赞亚,恩德.基于分类冗余字典稀疏表示的图像压缩方法[J].计算机工程,2017,43(9):287-293.
[6]	LUO Y,XU Y,JI H.Removing rain from a single image via discriminative sparse coding[C]//Proceedings of IEEE International Conference on Computer Vision.Washignton D.C.,USA:IEEE Press,2015:3397-3405.
[7]	RAMIREZ I,SPRECHMANN P,SAPIRO G.Classification and clustering via dictionary learning with structured incoherence and shared features[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washignton D.C.,USA:IEEE Press,2010:3501-3508.
[8]	GAO S,TSANG W H,MA Y.Learning category-specific dictionary and shared dictionary for fine-grained image categorization[J].IEEE Transactions on Image Processing,2013,23(2):623-634.
[9]	ZHANG Q,LI B.Discriminative K-SVD for dictionary learning in face recognition[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washignton D.C.,USA:IEEE Press,2010:2691-2698.
[10]	AHARON M,ELAD M,BRUCKSTEIN A.K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[11]	JIANG Z,LIN Z,DAVIS L S.Label consistent K-SVD:learning a discriminative dictionary for recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(11):2651-2664.
[12]	LI Z,LAI Z,XU Y,et al.A locality-constrained and label embedding dictionary learning algorithm for image classification[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(2):278-293.
[13]	MAIRAL J,BACH F,PONCE J,et al.Supervised dictionary learning[EB/OL].[2018-12-01].http://papers.nips.cc/paper/3448-supervised-dictionary-learning.
[14]	MAIRAL J,BACH F,PONCE J.Task-driven dictionary learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(4):791-804.
[15]	WANG Z,YANG J,NASRABADI N,et al.A max-margin perspective on sparse representation-based classification[C]//Proceedings of IEEE International Conference on Computer Vision.Washignton D.C.,USA:IEEE Press,2013:1217-1224.
[16]	YANG M,ZHANG L,FENG X,et al.Sparse representation based fisher discrimination dictionary learning for image classification[J].International Journal of Computer Vision,2014,109(3):209-232.
[17]	LIAN X C,LI Z,LU B L,et al.Max-margin dictionary learning for multiclass image categorization[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2010:157-170.
[18]	CAI Sijia,ZUO Wangmeng,ZHANG Lei,et al.Support vector guided dictionary learning[C]//Proceedings of ECCV'14.Berlin,Germany:Springer,2014:624-639.
[19]	VAPNIK V.Statistical learning theory[M].New York,USA:John Wiley and Sons Inc.,1998.
[20]	ZHANG L,YANG M,FENG X.Sparse representation or collaborative representation:which helps face recognition?[C]//Proceedings of 2014 International Conference on Computer Vision.New York,USA:ACM Press,2014:471-478.
[21]	CAI S,ZHANG L,ZUO W,et al.A probabilistic collaborative representation based approach for pattern classification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washignton D.C.,USA:IEEE Press,2016:2950-2959.
[22]	ZAFERIRIOU S,TEFAS A,PITAS I.Minimum class variance support vector machines[J].IEEE Transactions on Image Processing,2007,16(10):2551-2564.
[23]	KOTSIA I,PIATS I,ZAFEIRIOU S,et al.Novel multiclass classifiers based on the minimization of the within-class variance[J].IEEE Transactions on Neural Networks,2009,20(1):14-34.
[24]	ZHENG M,BU J,CHEN C,et al.Graph regularized sparse coding for image representation[J].IEEE Transactions on Image Processing,2011,20(5):1327-1336.
[25]	WANG D,KONG S.A classification-oriented dictionary learning model:explicitly learning the particularity and commonality across categories[J].Pattern Recognition,2014,47(2):885-898.
[26]	WANG J,LU C,WANG M,et al.Robust face recognition via adaptive sparse representation[J].IEEE Transactions on Cybernetics,2014,44(12):2368-2378.
[27]	HULL J J.A database for handwritten text recognition research[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,16(5):550-554.
[28]	ZHENG Zhang,YONG Xu,LING Shao,et al.Discriminative block-diagonal representation learning for image recognition[J].IEEE Transactions on Neural Networks and Learning Systems,2017,29(7):3111-3125.
[29]	LEE K C,HO J,KRIEGMAN D J.Acquiring linear subspaces for face recognition under variable lighting[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(5):684-698.
[30]	GEORGHIADES A S,BELHUMEUR P N,KRIEGMAN D J.From few to many:illumination cone models for face recognition under variable lighting and pose[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,23(6):643-660.
[31]	SAMARIA F S,HARTER A.Parameterization of a stochastic model for human face identification[C]//Proceedings of the 2nd IEEE Workshop on Applications of Computer Vision.Washington D.C.,USA:IEEE Press,1994:138-142.
[32]	MARTINEZA M,BEAVENTE R.The AR face database[EB/OL].[2018-12-01].http://www.cat.uab.es/Public/Publications/1998/MaB1998/.
[33]	NENE S A,NAYAR S K,MURASE H.Columbia object image library(COIL-20):CUCS-005-96[R].New York,USA:Columbia University,1996.
[34]	MURASE H,NAYAR S K.Visual learning and recognition of 3D objects from appearance[J].International Journal of Computer Vision,1995,14(1):5-24.
[35]	LI F F,FERGUS R,PERONA P.Learning generative visual models from few training examples:an incremental Bayesian approach tested on 101 object categories[C]//Proceedings of Conference on Computer Vision and Pattern Recognition Workshop.Washington D.C.,USA:IEEE Press,2004:178.
[36]	XU Yong,ZHANG Zheng,LU Guangming,et al.Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification[J].Pattern Recognition,2016,54:68-82.

Please choose a citation manager

Content to export