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Estimating Visual Saliency Through Single Image Optimization
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DOI：10.1109/LSP.2013.2268868.png)
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摘要
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This letter presents a novel approach for visual saliency estimation through single image optimization. Instead of directly mapping visual features to saliency values with a unified model, we treat regional saliency values as the optimization objective on each single image. By using a quadratic programming framework, our approach can adaptively optimize the regional saliency values on each specific image to simultaneously meet multiple saliency hypotheses on visual rarity, center-bias and mutual correlation. Experimental results show that our approach can outperform 14 state-of-the-art approaches on a public image benchmark.
Keyword:
Quadratic programming
single image optimization
visual saliency
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9.6
论文数:
1.1W
被引数:
1.7W
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