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Salient Object Detection via Structured Matrix Decomposition
Peng, Houwen1,2; Li, Bing1; Ling, Haibin2; Hu, Weiming1; Xiong, Weihua1; Maybank, Stephen J.3
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2017-04-01
Volume39Issue:4Pages:818-832
SubtypeArticle
AbstractLow-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.
KeywordSalient Object Detection Matrix Decomposition Low Rank Structured Sparsity Subspace Learning
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TPAMI.2016.2562626
WOS KeywordREGION DETECTION ; VISUAL-ATTENTION ; COMPLETION ; MODEL
Indexed BySCI
Language英语
Funding Organization973 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)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000397717600016
Citation statistics
Cited Times:48[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14432
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.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
Recommended Citation
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.
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