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Salient Object Detection via Structured Matrix Decomposition

文献类型：期刊论文

作者Peng, Houwen1,2; Li, Bing1; Ling, Haibin2; Hu, Weiming1; Xiong, Weihua1; Maybank, Stephen J.3
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2017-04-01
卷号39期号:4页码:818-832
关键词Salient Object Detection Matrix Decomposition Low Rank Structured Sparsity Subspace Learning
DOI10.1109/TPAMI.2016.2562626
文献子类Article
英文摘要Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e. g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.
WOS关键词REGION DETECTION ; VISUAL-ATTENTION ; COMPLETION ; MODEL
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000397717600016
资助机构973 basic research program of China(2014CB349303) ; Natural Science Foundation of China(61472421 ; Strategic Priority Research Program of the CAS(XDB02070003) ; US National Science Foundation Grants(IIS-1218156 ; 61370038 ; IIS-1350521) ; 61303086)
源URL[http://ir.ia.ac.cn/handle/173211/14432]  
专题自动化研究所_模式识别国家重点实验室_视频内容安全团队
作者单位1.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA
3.Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England
推荐引用方式
GB/T 7714		 Peng, Houwen,Li, Bing,Ling, Haibin,et al. Salient Object Detection via Structured Matrix Decomposition[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2017,39(4):818-832.
APA Peng, Houwen,Li, Bing,Ling, Haibin,Hu, Weiming,Xiong, Weihua,&Maybank, Stephen J..(2017).Salient Object Detection via Structured Matrix Decomposition.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,39(4),818-832.
MLA Peng, Houwen,et al."Salient Object Detection via Structured Matrix Decomposition".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 39.4(2017):818-832.

入库方式： OAI收割

来源：自动化研究所

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