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论文类型：期刊论文

发表时间：2016-01-01

发表刊物：MATHEMATICAL PROBLEMS IN ENGINEERING

收录刊物：SCIE、EI

卷号：2016

ISSN号：1024-123X

摘要：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.