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Proceedings Paper

Saliency detection algorithm based on LSC-RC
Author(s): Wei Wu; Weiye Tian; Ding Wang; Xin Luo; Yingfei Wu; Yu Zhang
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Paper Abstract

Image prominence is the most important region in an image, which can cause the visual attention and response of human beings. Preferentially allocating the computer resources for the image analysis and synthesis by the significant region is of great significance to improve the image area detecting. As a preprocessing of other disciplines in image processing field, the image prominence has widely applications in image retrieval and image segmentation. Among these applications, the super-pixel segmentation significance detection algorithm based on linear spectral clustering (LSC) has achieved good results. The significance detection algorithm proposed in this paper is better than the regional contrast ratio by replacing the method of regional formation in the latter with the linear spectral clustering image is super-pixel block. After combining with the latest depth learning method, the accuracy of the significant region detecting has a great promotion. At last, the superiority and feasibility of the super-pixel segmentation detection algorithm based on linear spectral clustering are proved by the comparative test.

Paper Details

Date Published: 19 February 2018
PDF: 11 pages
Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 106080D (19 February 2018);
Show Author Affiliations
Wei Wu, Wuhan Univ. of Technology (China)
Weiye Tian, Wuhan Univ. of Technology (China)
Ding Wang, Wuhan Univ. of Technology (China)
Xin Luo, Wuhan Univ. of Technology (China)
Yingfei Wu, Wuhan Univ. of Technology (China)
Yu Zhang, Wuhan Univ. of Technology (China)

Published in SPIE Proceedings Vol. 10608:
MIPPR 2017: Automatic Target Recognition and Navigation
Jianguo Liu; Jayaram K. Udupa; Hanyu Hong, Editor(s)

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