Regular Articles

Salient object detection: manifold-based similarity adaptation approach

[+] Author Affiliations
Jingbo Zhou

Huaiyin Institute of Technology, Faculty of Computer Engineering, Huai’an 223003, China

Yongfeng Ren

Huaiyin Institute of Technology, Faculty of Computer Engineering, Huai’an 223003, China

Hohai University, College of Computer and Information, Nanjing 211100, China

Yunyang Yan

Huaiyin Institute of Technology, Faculty of Computer Engineering, Huai’an 223003, China

Shangbing Gao

Huaiyin Institute of Technology, Faculty of Computer Engineering, Huai’an 223003, China

J. Electron. Imaging. 23(6), 063004 (Nov 05, 2014). doi:10.1117/1.JEI.23.6.063004
History: Received June 18, 2014; Revised August 30, 2014; Accepted October 6, 2014
Text Size: A A A

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.

Figures in this Article
© 2014 SPIE and IS&T

Citation

Jingbo Zhou ; Yongfeng Ren ; Yunyang Yan and Shangbing Gao
"Salient object detection: manifold-based similarity adaptation approach", J. Electron. Imaging. 23(6), 063004 (Nov 05, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.6.063004


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.