Algorithms for image saliency via sparse representation and multi-scale inputs image retargeting
Saliency detection is an important yet challenging task in computer vision. In this report we investigate the use of sparse coding over redundant dictionary for saliency detection. We attempt to present a small fraction of the growing knowledge regarding sparse representation over redundant dictiona...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2012
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| Online Access: | https://hdl.handle.net/10356/50583 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Saliency detection is an important yet challenging task in computer vision. In this report we investigate the use of sparse coding over redundant dictionary for saliency detection. We attempt to present a small fraction of the growing knowledge regarding sparse representation over redundant dictionary and discuss some potential usage of this powerful tool for saliency detection task. We propose a new algorithm for saliency detection based on the likelihood that images patch can be encoded sparsely using a dictionary learned from other patches. Experimental results based on saliency ground of truth of 1000 real images shows a superior performance of the renew algorithm in comparison with other existing saliency algorithms. |
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