21 July 2017 Salient detection based on improved graph models
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Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042018 (2017) https://doi.org/10.1117/12.2281766
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China
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.
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Yiming Zhang, Yiming Zhang, Jing Han, Jing Han, Yi Zhang, Yi Zhang, Lianfa Bai, Lianfa Bai, } "Salient detection based on improved graph models", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042018 (21 July 2017); doi: 10.1117/12.2281766; https://doi.org/10.1117/12.2281766
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