Robust Image Analysis With Sparse Representation on Quantized Visual Features
Bao, Bing-Kun1,2; Zhu, Guangyu3; Shen, Jialie4; Yan, Shuicheng5
发表期刊IEEE TRANSACTIONS ON IMAGE PROCESSING
2013-03-01
卷号22期号:3页码:860-871
文章类型Article
摘要Recent 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.
关键词Image Classification Quantized Visual Feature Sparse Representation
WOS标题词Science & Technology ; Technology
关键词[WOS]LOCAL BINARY PATTERNS ; FACE RECOGNITION ; CLASSIFICATION ; REGRESSION ; SHRINKAGE ; ALGORITHM ; SELECTION ; SYSTEMS ; SCALE ; LASSO
收录类别SCI
语种英语
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000318014300002
引用统计
被引频次:26[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/8861
专题模式识别国家重点实验室_多媒体计算与图形学
作者单位1.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
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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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