Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016, Article ID 8740593, 11 pages
Research Article

Graph-Based Salient Region Detection through Linear Neighborhoods

1School of Computer Science and Technology, Dalian University of Technology, No. 2 Linggong Road, Dalian 116024, China
2School of Computer and Information Technology, Liaoning Normal University, No. 850 Huanghe Road, Dalian 116024, China

Received 17 March 2016; Accepted 9 May 2016

Academic Editor: Chanho Jung

Copyright © 2016 Lijuan Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Pairwise neighboring relationships estimated by Gaussian weight function have been extensively adopted in the graph-based salient region detection methods recently. However, the learning of the parameters remains a problem as nonoptimal models will affect the detection results significantly. To tackle this challenge, we first apply the adjacent information provided by all neighbors of each node to construct the undirected weight graph, based on the assumption that every node can be optimally reconstructed by a linear combination of its neighbors. Then, the saliency detection is modeled as the process of graph labelling by learning from partially selected seeds (labeled data) in the graph. The promising experimental results presented on some datasets demonstrate the effectiveness and reliability of our proposed graph-based saliency detection method through linear neighborhoods.