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Proceedings Paper

Saliency detection based on multi-instance images learning
Author(s): Shouhong Wan; Peiquan Jin; Lihua Yue; Qian Huang
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Paper Abstract

Existing visual saliency detection methods are usually based on single image, however, without priori knowledge, the contents of single image are ambiguous, so visual saliency detection based on single image can’t extract region of interest. To solve it, we propose a novel saliency detection based on multi-instance images. Our method considers human’s visual psychological factors and measures visual saliency based on global contrast, local contrast and sparsity. It firstly uses multi-instance learning to get the center of clustering, and then computes feature relative dispersion. By fusing different weighted feature saliency map, the final synthesize saliency map is generated. Comparing with other saliency detection methods, our method increases the rate of hit.

Paper Details

Date Published: 6 July 2015
PDF: 8 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96310O (6 July 2015); doi: 10.1117/12.2197036
Show Author Affiliations
Shouhong Wan, Univ. of Science and Technology of China (China)
Key Lab. of Electromagnetic Space Information (China)
Peiquan Jin, Univ. of Science and Technology of China (China)
Key Lab. of Electromagnetic Space Information (China)
Lihua Yue, Univ. of Science and Technology of China (China)
Key Lab. of Electromagnetic Space Information (China)
Qian Huang, Univ. of Science and Technology of China (China)
Key Lab. of Electromagnetic Space Information (China)


Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

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