Learning visual saliency

Qi Zhao, Christof Koch

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

7 Scopus citations

Abstract

Inspired by the primate visual system, computational saliency models decompose the visual input into a set of feature maps across spatial scales. In the standard approach, the feature maps of the pre-specified channels are summed to yield the final saliency map. We study the feature integration problem and propose two improved strategies: first, we learn a weighted linear combination of features using the constraint linear regression algorithm. We further propose an AdaBoost based algorithm to approach the feature selection, thresholding, weight assignment, and nonlinear integration in a single principled framework. Extensive quantitative evaluations of the new models are conducted using four public datasets, and improvements on model predictability power are shown.

Original languageEnglish (US)
Title of host publication2011 45th Annual Conference on Information Sciences and Systems, CISS 2011
DOIs
StatePublished - Jun 6 2011
Event2011 45th Annual Conference on Information Sciences and Systems, CISS 2011 - Baltimore, MD, United States
Duration: Mar 23 2011Mar 25 2011

Publication series

Name2011 45th Annual Conference on Information Sciences and Systems, CISS 2011

Other

Other2011 45th Annual Conference on Information Sciences and Systems, CISS 2011
CountryUnited States
CityBaltimore, MD
Period3/23/113/25/11

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