CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Beyond Explicit Codebook Generation: Visual Representation Using Implicitly Transferred Codebooks
Zhang, Chunjie1,2; Cheng, Jian3; Liu, Jing3; Pang, Junbiao4; Huang, Qingming1,2,5; Tian, Qi6
AbstractThe bag-of-visual-words model plays a very important role for visual applications. Local features are first extracted and then encoded to get the histogram-based image representation. To encode local features, a proper codebook is needed. Usually, the codebook has to be generated for each data set which means the codebook is data set dependent. Besides, the codebook may be biased when we only have a limited number of training images. Moreover, the codebook has to be pre-learned which cannot be updated quickly, especially when applied for online visual applications. To solve the problems mentioned above, in this paper, we propose a novel implicit codebook transfer method for visual representation. Instead of explicitly generating the codebook for the new data set, we try to make use of pre-learned codebooks using non-linear transfer. This is achieved by transferring the pre-learned codebooks with non-linear transformation and use them to reconstruct local features with sparsity constraints. The codebook does not need to be explicitly generated but can be implicitly transferred. In this way, we are able to make use of pre-learned codebooks for new visual applications by implicitly learning the codebook and the corresponding encoding parameters for image representation. We apply the proposed method for image classification and evaluate the performance on several public image data sets. Experimental results demonstrate the effectiveness and efficiency of the proposed method.
KeywordCodebook Transfer Image Representation Classification Reconstruction Sparse Constraint
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding OrganizationOpen Project through the Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences ; National Basic Research Program of China (973 Program)(2012CB316400 ; National Natural Science Foundation of China(61025011 ; ARO(W911NF-15-1-0290 ; Faculty Research Awards by NEC Laboratories of America ; 2015CB351802) ; 61170127 ; W911NF-12-1-0057) ; 61202234 ; 61272329 ; 61303154 ; 61332016 ; 61429201)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000369538200002
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Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100864, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intell Info Proc, Beijing 100864, Peoples R China
6.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
Recommended Citation
GB/T 7714
Zhang, Chunjie,Cheng, Jian,Liu, Jing,et al. Beyond Explicit Codebook Generation: Visual Representation Using Implicitly Transferred Codebooks[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(12):5777-5788.
APA Zhang, Chunjie,Cheng, Jian,Liu, Jing,Pang, Junbiao,Huang, Qingming,&Tian, Qi.(2015).Beyond Explicit Codebook Generation: Visual Representation Using Implicitly Transferred Codebooks.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(12),5777-5788.
MLA Zhang, Chunjie,et al."Beyond Explicit Codebook Generation: Visual Representation Using Implicitly Transferred Codebooks".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.12(2015):5777-5788.
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