文献类型：会议论文

英文题名：Image Representation via Sub-dictionary based Sparse Coding

作者：Xu, Bingxin[1];Yin, Qian[2];Guo, Ping[2];Liu, Hongzhe[1]

第一作者：徐冰心

通讯作者：Xu, BX[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Normal Univ, Image Proc & Pattern Recognit Lab, Beijing 100875, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[11417103]北京联合大学北京市信息服务工程重点实验室;[11417]北京联合大学;

会议论文集：International Joint Conference on Neural Networks (IJCNN)

会议日期：JUL 24-29, 2016

会议地点：Vancouver, CANADA

语种：英文

外文关键词：Image coding

摘要：In this paper, a sub-dictionary based sparse coding method is proposed for image representation. The novel sparse coding method substitutes a new regularization item for L1-norm in the sparse representation model. The proposed sparse coding method involves a series of sub-dictionaries. Each sub-dictionary contains all the training samples except for those from one particular category. For the test sample to be represented, all the sub-dictionaries should linearly represent it apart from the one that does not contain samples from that label, and this sub-dictionary is called irrelevant sub-dictionary. This new regularization item restricts the sparsity of each sub-dictionary's residual, and this restriction is helpful for classification. The experimental results demonstrate that the proposed method is superior to the previous related sparse representation based classification.