Salient object detection: manifold-based similarity adaptation approach

Jingbo Zhou; Yongfeng Ren; Yunyang Yan; Shangbing Gao

doi:10.1117/1.JEI.23.6.063004

5 November 2014 Salient object detection: manifold-based similarity adaptation approach

Jingbo Zhou, Yongfeng Ren, Yunyang Yan, Shangbing Gao

Author Affiliations +

Journal of Electronic Imaging, Vol. 23, Issue 6, 063004 (November 2014). https://doi.org/10.1117/1.JEI.23.6.063004

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.

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Jingbo Zhou, Yongfeng Ren, Yunyang Yan, and Shangbing Gao "Salient object detection: manifold-based similarity adaptation approach," Journal of Electronic Imaging 23(6), 063004 (5 November 2014). https://doi.org/10.1117/1.JEI.23.6.063004

Published: 5 November 2014

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