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A new algorithm for bottom-up saliency estimation is proposed. Based on the sparse coding model, a power spectral filter is proposed to eliminate the second-order residual correlation, which suppresses the global repeated items effectively. In addition, aiming at modeling the mechanism of the human retina prior response to high-contrast stimuli, the effect of color context is considered. Experiments on the three publicly available databases and some psychophysical images show that the proposed model is comparable with the state-of-the-art saliency models, which not only highlights the salient objects in a complex environment but also pops up them uniformly. 相似文献
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