Saliency detection based on diffusion maps |
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Authors: | Zhi Yang DeHua LiJin-Gang Yu |
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Affiliation: | School of Automation, Huazhong University of Science & Technology, Wuhan 430074, China |
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Abstract: | Visual saliency has recently attracted lots of research interest in the computer vision community. In this paper, we propose a novel computational model for bottom-up saliency detection based on manifold learning. A typical graph-based manifold learning algorithm, namely the diffusion maps, is adopted for establishing our saliency model. In the proposed method, firstly, a graph is constructed using low-level image features. Then, the diffusion maps algorithm is performed to learn the diffusion distances at different time, which are utilized to derive the saliency measure. Compared to existing saliency models, our method has the advantage of being able to capture the intrinsic nonlinear structures in the original feature space. Experimental results on publicly available data demonstrate that our method outperforms the state-of-the-art saliency models, both qualitatively and quantitatively. |
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Keywords: | Saliency detection Graph-based manifold learning Diffusion maps Diffusion distance |
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