Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 39, Issue 4, Pages 818-832Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2016.2562626
Keywords
Salient object detection; matrix decomposition; low rank; structured sparsity; subspace learning
Funding
- 973 basic research program of China [2014CB349303]
- Natural Science Foundation of China [61472421, 61370038, 61303086]
- Strategic Priority Research Program of the CAS [XDB02070003]
- US National Science Foundation Grants [IIS-1218156, IIS-1350521]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1449860] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1350521] Funding Source: National Science Foundation
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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.
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