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Journal of Electronic Imaging

Salient object detection: manifold-based similarity adaptation approach
Author(s): Jingbo Zhou; Yongfeng Ren; Yunyang Yan; Shangbing Gao
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Paper Abstract

A saliency detection algorithm based on manifold-based similarity adaptation is proposed. The proposed algorithm is divided into three steps. First, we segment an input image into superpixels, which are represented as the nodes in a graph. Second, a new similarity measurement is used in the proposed algorithm. The weight matrix of the graph, which indicates the similarities between the nodes, uses a similarity-based method. It also captures the manifold structure of the image patches, in which the graph edges are determined in a data adaptive manner in terms of both similarity and manifold structure. Then, we use local reconstruction method as a diffusion method to obtain the saliency maps. The objective function in the proposed method is based on local reconstruction, with which estimated weights capture the manifold structure. Experiments on four bench-mark databases demonstrate the accuracy and robustness of the proposed method.

Paper Details

Date Published: 5 November 2014
PDF: 10 pages
J. Electron. Imaging. 23(6) 063004 doi: 10.1117/1.JEI.23.6.063004
Published in: Journal of Electronic Imaging Volume 23, Issue 6
Show Author Affiliations
Jingbo Zhou, Huaiyin Institute of Technology (China)
Yongfeng Ren, Huaiyin Institute of Technology (China)
Hohai University (China)
Yunyang Yan, Huaiyin Institute of Technology (China)
Shangbing Gao, Huaiyin Institute of Technology (China)

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