Saliency Detection with Sparse Prototypes: An Approach Based on Multi-Dictionary Sparse Encoding
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This paper proposes a bottom-up saliency detection algorithm based on multi-dictionary sparse recovery. Firstly, the SLIC algorithm is used to segment the image into superpixels in multilevel and atoms with a high background possibility are selected from the boundary superpixels to construct the multidictionary. Secondly, sparse recovery of the entire image is achieved using multi-dictionary to get subsaliency maps from the perspective of sparse recovery errors. The final saliency map is generated in a weighted fusion manner. Experimental results on three public datasets demonstrate the effectiveness of our model.
2016 ◽
Vol 2016
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pp. 1-18
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2011 ◽
Vol 403-408
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pp. 1927-1932
2014 ◽
Vol 644-650
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pp. 4603-4606
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Vol 2013
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pp. 1-9
2019 ◽
Vol 37
(3)
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pp. 503-508
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2014 ◽
Vol 28
(04)
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pp. 1454001
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