Sparse likelihood saliency detection
This paper addresses the problem of detection salient regions in images by exploiting the redundancy in image patches. We assume that redundant patches are more likely to be sparsely represented by other patches in the image while salient patches are not. Such sparse likelihood can be measured via L...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/98774 http://hdl.handle.net/10220/13402 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This paper addresses the problem of detection salient regions in images by exploiting the redundancy in image patches. We assume that redundant patches are more likely to be sparsely represented by other patches in the image while salient patches are not. Such sparse likelihood can be measured via L1-minimization by finding the sparse representation of an image patch based on a dictionary constructed using all other patches from the input image. We show that this approach leads to a robust saliency algorithm and the evaluation based on a database of 1000 images demonstrates that our algorithm achieves significant improvement over existing methods. |
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