基于带汇点Laplace扩散模型的显著目标检测

doi:10.11999/JEIT161296

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Abstract

Based on Laplace similarity metrics, corresponding diffusion-based saliency models are proposed according to different clusters (sparse or dense) of salient seeds in the two-stage detection, a diffusion-based two-stage complementary method for salient object detection is therefore investigated. Especially for the introduction of sink points in the second stage, saliency maps obtained by this proposed method can well restrain background parts, as well as become more robust with the change of control factor α. Experiments show that different diffusion models will cause diversities of saliency diffusion degree when salient seeds are determined. In addition, the two-stage Laplace-based diffusion model with sink points is more effective and robust than other two-stage diffusion models. Meanwhile, the proposed algorithm is superior over the existing five state-of-the-art methods in terms of different metrics. This exactly shows that the similarity metrics method applied to image retrieval and classification is also available for salient objects detection.

Key words： Object detection Saliency Sink points Laplace matrix Diffusion model

Received: 28 November 2016 Published: 14 June 2017

PACS:

TP391

Fund:

The National Natural Science Foundation of China (51475086), The Natural Science Foundation of Liaoning Province (2014020026)

Corresponding Authors: WANG Baoyan E-mail: wangbaoyan2005@163.com

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	WANG Baoyan
	ZHANG Tie
	WANG Xingang

Cite this article:

WANG Baoyan,ZHANG Tie,WANG Xingang. Salient Object Detection Based on Laplace Diffusion Models with Sink Points[J]. JEIT, 2017, 39(8): 1934-1941.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT161296 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I8/1934

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