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Proceedings Paper

Saliency detection based on manifold learning
Author(s): Zhi Yang; DeHua Li; Jie Wang; Xuan Li
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Paper 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 graphbased manifold learning algorithm, namely the diffusion map, is adopted for establishing our saliency model. In the proposed method, firstly, a graph is constructed using low-level image features. Then, the diffusion map algorithm is performed to learn the diffusion distances, 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. Moreover, due to the inherent characteristics of the diffusion map algorithm, our method can deal with the multi-scale issue effectively, which is crucial to any saliency model. Experimental results on publicly available data demonstrate that our method outperforms the state-of-the-art saliency models, both qualitatively and quantitatively.

Paper Details

Date Published: 27 October 2013
PDF: 6 pages
Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 891906 (27 October 2013); doi: 10.1117/12.2030837
Show Author Affiliations
Zhi Yang, Huazhong Univ. of Science and Technology (China)
DeHua Li, Huazhong Univ. of Science and Technology (China)
Jie Wang, Huazhong Univ. of Science and Technology (China)
Xuan Li, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8919:
MIPPR 2013: Pattern Recognition and Computer Vision
Zhiguo Cao, Editor(s)

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