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

文献类型：期刊论文

作者Zhang, Chunjie1,2,3; Liang, Chao4; Li, Liang2,3; Liu, Jing5; Huang, Qingming2,3,6; Tian, Qi7
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2017-07-01
卷号28期号:7页码:1550-1559
关键词Fine Grained Image Representation Semantic Space Visual Recognition
DOI10.1109/TNNLS.2016.2545112
文献子类Article
英文摘要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.
WOS关键词FEATURES ; CATEGORIZATION ; DICTIONARY ; KERNEL ; RECOGNITION ; FORESTS ; SEARCH
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000404048300006
资助机构Open 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)
源URL[http://ir.ia.ac.cn/handle/173211/15235]  
专题自动化研究所_模式识别国家重点实验室_图像与视频分析团队
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.

入库方式： OAI收割

来源：自动化研究所

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