>
    当前位置:首页>科研成果
论文编号:
第一作者所在部门:
中文论文题目: Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks
英文论文题目: Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks
论文题目英文:
作者: Zhang, Chunjie
论文出处:
刊物名称: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
年: 2017
卷: 28
期: 7
页: 1550-1559
联系作者:
收录类别:
影响因子:
摘要: This paper tries to separate fine-grained images by jointly learning the encoding parameters and codebooks through low-rank sparse coding (LRSC) with general and class-specific codebook generation. Instead of treating each local feature independently, we encode the local features within a spatial region jointly by LRSC. This ensures that the spatially nearby local features with similar visual characters are encoded by correlated parameters. In this way, we can make the encoded parameters more consistent for fine-grained image representation. Besides, we also learn a general codebook and a number of class-specific codebooks in combination with the encoding scheme. Since images of fine-grained classes are visually similar, the difference is relatively small between the general codebook and each class-specific codebook. We impose sparsity constraints to model this relationship. Moreover, the incoherences with different codebooks and class-specific codebooks are jointly considered. We evaluate the proposed method on several public image data sets. The experimental results show that by learning general and classspecific codebooks with the joint encoding of local features, we are able to model the differences among different fine-grained classes than many other fine-grained image classification methods.
英文摘要:
外单位作者单位:
备注:

关闭窗口