Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks

doi:10.1109/TNNLS.2016.2545112

	Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks
	Zhang, Chunjie 1,2,3; Liang, Chao 4; Li, Liang 2,3; Liu, Jing 5; Huang, Qingming 2,3,6; Tian, Qi 7
	2017-07-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	28 期号:7 页码:1550-1559
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.
关键词	Fine grained image representation semantic space visual recognition
DOI	10.1109/TNNLS.2016.2545112
收录类别	SCI
语种	英语
资助项目	Open Project of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences ; National Basic Research Program of China[2012CB316400] ; National Basic Research Program of China[2015CB351802] ; National Natural Science Foundation of China[61272329] ; National Natural Science Foundation of China[61303114] ; National Natural Science Foundation of China[61303154] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61402431]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000404048300006
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：53[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7089
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Chunjie
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China 4.Wuhan Univ, Sch Comp, Natl Engn Res Ctr Multimedia Software, Wuhan 430072, Peoples R China 5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 6.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 7.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
推荐引用方式 GB/T 7714	Zhang, Chunjie,Liang, Chao,Li, Liang,et al. Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2017,28(7):1550-1559.
APA	Zhang, Chunjie,Liang, Chao,Li, Liang,Liu, Jing,Huang, Qingming,&Tian, Qi.(2017).Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,28(7),1550-1559.
MLA	Zhang, Chunjie,et al."Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 28.7(2017):1550-1559.