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Robust Image Analysis With Sparse Representation on Quantized Visual Features
Bao, Bing-Kun1,2; Zhu, Guangyu3; Shen, Jialie4; Yan, Shuicheng5
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2013-03-01
Volume22Issue:3Pages:860-871
SubtypeArticle
AbstractRecent techniques based on sparse representation (SR) have demonstrated promising performance in high-level visual recognition, exemplified by the highly accurate face recognition under occlusion and other sparse corruptions. Most research in this area has focused on classification algorithms using raw image pixels, and very few have been proposed to utilize the quantized visual features, such as the popular bag-of-words feature abstraction. In such cases, besides the inherent quantization errors, ambiguity associated with visual word assignment and misdetection of feature points, due to factors such as visual occlusions and noises, constitutes the major cause of dense corruptions of the quantized representation. The dense corruptions can jeopardize the decision process by distorting the patterns of the sparse reconstruction coefficients. In this paper, we aim to eliminate the corruptions and achieve robust image analysis with SR. Toward this goal, we introduce two transfer processes (ambiguity transfer and mis-detection transfer) to account for the two major sources of corruption as discussed. By reasonably assuming the rarity of the two kinds of distortion processes, we augment the original SR-based reconstruction objective with l(0)-norm regularization on the transfer terms to encourage sparsity and, hence, discourage dense distortion/transfer. Computationally, we relax the nonconvex l(0)-norm optimization into a convex l(1)-norm optimization problem, and employ the accelerated proximal gradient method to optimize the convergence provable updating procedure. Extensive experiments on four benchmark datasets, Caltech-101, Caltech-256, Corel-5k, and CMU pose, illumination, and expression, manifest the necessity of removing the quantization corruptions and the various advantages of the proposed framework.
KeywordImage Classification Quantized Visual Feature Sparse Representation
WOS HeadingsScience & Technology ; Technology
WOS KeywordLOCAL BINARY PATTERNS ; FACE RECOGNITION ; CLASSIFICATION ; REGRESSION ; SHRINKAGE ; ALGORITHM ; SELECTION ; SYSTEMS ; SCALE ; LASSO
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000318014300002
Citation statistics
Cited Times:34[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8861
Collection模式识别国家重点实验室_多媒体计算与图形学
Affiliation1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.China Singapore Inst Digital Media, Singapore 119613, Singapore
3.Univ Calif Berkeley, Berkeley, CA 94720 USA
4.Singapore Management Univ, Singapore 188065, Singapore
5.Natl Univ Singapore, Singapore 117576, Singapore
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Bao, Bing-Kun,Zhu, Guangyu,Shen, Jialie,et al. Robust Image Analysis With Sparse Representation on Quantized Visual Features[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2013,22(3):860-871.
APA Bao, Bing-Kun,Zhu, Guangyu,Shen, Jialie,&Yan, Shuicheng.(2013).Robust Image Analysis With Sparse Representation on Quantized Visual Features.IEEE TRANSACTIONS ON IMAGE PROCESSING,22(3),860-871.
MLA Bao, Bing-Kun,et al."Robust Image Analysis With Sparse Representation on Quantized Visual Features".IEEE TRANSACTIONS ON IMAGE PROCESSING 22.3(2013):860-871.
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