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Salient detection based on improved graph models
Author(s): Yiming Zhang; Jing Han; Yi Zhang; Lianfa Bai
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Paper Abstract

In this paper, we propose a novel approach to detect visual saliency based on improved graph models. The graph-based manifold ranking algorithm adopts k neighbor constraint and close-loop constraint to construct a graph model. In the first stage of the algorithm, k = 2 neighbor constraint reduces the differences between foreground nodes and boundary nodes and the saliency value of the nodes which are close to the center of the image tends to be larger. So some background nodes are selected as foreground queries in the second stage and some background will be highlighted. To eliminate such defect of the algorithm, we use a simple graph structure containing close-loop constraint in the first stage and update the graph structure by expanding close-loop constraint to calculate a new ranking matrix, which is then used to calculate the final saliency map. Experiments in the MSRA1000 database prove that our method outperforms the classical algorithm.

Paper Details

Date Published: 21 July 2017
PDF: 7 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042018 (21 July 2017); doi: 10.1117/12.2281766
Show Author Affiliations
Yiming Zhang, Nanjing Univ. of Science and Technology (China)
Jing Han, Nanjing Univ. of Science and Technology (China)
Yi Zhang, Nanjing Univ. of Science and Technology (China)
Lianfa Bai, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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