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Proceedings Paper

A competition-based image saliency model
Author(s): Yang Li; Xuanqin Mou
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Paper Abstract

Competition for visual representation is an important mechanism for selective visual attention. The traditional global distinctiveness based saliency models usually compute the distinctiveness to measure saliency via comparing the difference of image patches in various spaces. In this paper, we propose to use an improved neural competition model to replace the comparison. The pairwise competition responses for a patch to all of the other patches are summed up to represent the distinctiveness of that patch. Particularly, the competition response is computed by a neural competition model with the dissimilarity bias and the gradient based feature inputs. Experimental results validate that the proposed model presents high effectiveness in saliency detection by outperforming nine state-of-the-art models.

Paper Details

Date Published: 18 November 2019
PDF: 8 pages
Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 111871N (18 November 2019); doi: 10.1117/12.2537895
Show Author Affiliations
Yang Li, Xi'an Jiaotong Univ. (China)
Xuanqin Mou, Xi'an Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 11187:
Optoelectronic Imaging and Multimedia Technology VI
Qionghai Dai; Tsutomu Shimura; Zhenrong Zheng, Editor(s)

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