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Salient Object Detection via Structured Matrix Decomposition
2017
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
doi:10.1109/tpami.2016.2562626
pmid:28113696
fatcat:4rnbcixgxvahdi3qh7rfeibsgm