CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Fine-Grained Image Classification via Low-Rank Sparse Coding With General and Class-Specific Codebooks
Zhang, Chunjie1,2,3; Liang, Chao4; Li, Liang2,3; Liu, Jing5; Huang, Qingming2,3,6; Tian, Qi7
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2017-07-01
Volume28Issue:7Pages:1550-1559
SubtypeArticle
AbstractThis 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.
KeywordFine Grained Image Representation Semantic Space Visual Recognition
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TNNLS.2016.2545112
WOS KeywordFEATURES ; CATEGORIZATION ; DICTIONARY ; KERNEL ; RECOGNITION ; FORESTS ; SEARCH
Indexed BySCI
Language英语
Funding OrganizationOpen Project of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences ; National Basic Research Program of China(2012CB316400 ; National Natural Science Foundation of China(61272329 ; 2015CB351802) ; 61303114 ; 61303154 ; 61332016 ; 61402431)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000404048300006
Citation statistics
Cited Times:19[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15235
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.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
Recommended Citation
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.
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