Saliency detection via divergence analysis: A unified perspective

Jia Bin Huang, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A number of bottom-up saliency detection algorithms have been proposed in the literature. Since these have been developed from intuition and principles inspired by psychophysical studies of human vision, the theoretical relations among them are unclear. In this paper, we present a unifying perspective. Saliency of an image area is defined in terms of divergence between certain feature distributions estimated from the central part and its surround. We show that various, seemingly different saliency estimation algorithms are in fact closely related. We also discuss some commonly used center-surround selection strategies. Experiments with two datasets are presented to quantify the relative advantages of these algorithms.

Original languageEnglish (US)
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages2748-2751
Number of pages4
StatePublished - Dec 1 2012
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: Nov 11 2012Nov 15 2012

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period11/11/1211/15/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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