Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 图像与视频分析团队

	Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition
	Zhang, Chunjie1 ; Liu, Jing2 ; Liang, Chao 3; Xue, Zhe 1; Pang, Junbiao 4; Huang, Qingming 1,5
刊名	COMPUTER VISION AND IMAGE UNDERSTANDING
	2014-06-01
卷号	123 页码:14-22
关键词	Sparse coding Image classification Low-rank decomposition Non-negative Correlation constrained
英文摘要	We propose an image classification framework by leveraging the non-negative sparse coding, correlation constrained low rank and sparse matrix decomposition technique (CCLR-Sc+SPM). First, we propose a new non-negative sparse coding along with max pooling and spatial pyramid matching method (Sc+SPM) to extract local feature's information in order to represent images, where non-negative sparse coding is used to encode local features. Max pooling along with spatial pyramid matching (SPM) is then utilized to get the feature vectors to represent images. Second, we propose to leverage the correlation constrained low-rank and sparse matrix recovery technique to decompose the feature vectors of images into a low-rank matrix and a sparse error matrix by considering the correlations between images. To incorporate the common and specific attributes into the image representation, we still adopt the idea of sparse coding to recode the Sc+SPM representation of each image. In particular, we collect the columns of the both matrixes as the bases and use the coding parameters as the updated image representation by learning them through the locality-constrained linear coding (LLC). Finally, linear SVM classifier is trained for final classification. Experimental results show that the proposed method achieves or outperforms the state-of-the-art results on several benchmarks. (C) 2014 Elsevier Inc. All rights reserved.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
关键词[WOS]	FACE RECOGNITION ; REPRESENTATION ; KERNEL
收录类别	SCI
语种	英语
WOS记录号	WOS:000335488600002
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3336]
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.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Wuhan Univ, Natl Engn Res Ctr Multimedia Software, Sch Comp, Wuhan 430072, Peoples R China 4.Beijing Univ Technol, Coll Comp Sci & Technol, Beijing 100124, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intell Info Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Chunjie,Liu, Jing,Liang, Chao,et al. Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2014,123:14-22.
APA	Zhang, Chunjie,Liu, Jing,Liang, Chao,Xue, Zhe,Pang, Junbiao,&Huang, Qingming.(2014).Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition.COMPUTER VISION AND IMAGE UNDERSTANDING,123,14-22.
MLA	Zhang, Chunjie,et al."Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition".COMPUTER VISION AND IMAGE UNDERSTANDING 123(2014):14-22.