A competition-based image saliency model

Yang Li; Xuanqin Mou

doi:10.1117/12.2537895

18 November 2019 A competition-based image saliency model

Yang Li, Xuanqin Mou

Proceedings Volume 11187, Optoelectronic Imaging and Multimedia Technology VI; 111871N (2019) https://doi.org/10.1117/12.2537895
Event: SPIE/COS Photonics Asia, 2019, Hangzhou, China

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.

Citation Download Citation

Yang Li and Xuanqin Mou "A competition-based image saliency model", Proc. SPIE 11187, Optoelectronic Imaging and Multimedia Technology VI, 111871N (18 November 2019); https://doi.org/10.1117/12.2537895

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