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Sparse representation via L2,p norm for image classification(PDF)


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Sparse representation via L2,p norm for image classification
Shi Zhongrong1Wang Sheng2Liu Chuancai1
1.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China; 2.Institute of Image Processing and Pattern Recognition,Henan University,Kaifeng 475004,China
image classification sparse representation sparse classification matrix norm sparse coding dictionary learning sparse regularization sparse-inducing norm
For the sparse representation-based classification method,since the non-zero elements of sparse coefficients with the same class are concentrated in a few rows,we propose to regularize the coefficient matrix using an l2,p matrix norm.In the training phase of the algorithm,the objective function consists of three parts:reconstruction error,sparse regularization,and inconsistency of reconstruction coefficients between different classes.The sparse regularization term is implemented by an l2,p matrix norm.In the test phase,the sparse reconstruction coefficient of a new sample is found using the dictionary learned in the training phase.Finally,the new sample is classified according to the sparse reconstruction coefficient.Compared with the traditional classification method based on sparse representation,the proposed method does not process a single sample to find its sparse reconstruction coefficient,but the whole sample matrix can be processed,this takes full advantage of the similarity among the same class.The experimental results show that this method can improve the accuracies of image classification 20.11%,20.88%,and 2.13% compared with a baseline SRC(Sparse representation based classification)method in AR,Extended Yale B,and Fifteen Scene Category databases,respectively.This method makes full use of the similarity of the same class and improves the accuracy of the image classification based on sparse representation.


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Last Update: 2017-02-28