Chemical Reaction Optimization for Feature Combination in Bio-inspired Visual Attention

DOI

10.1080/18756891.2015.1036220How to use a DOI?

Keywords

bio-inspired visual attention, Chemical Reaction Optimization (CRO), feature combination, saliency

Abstract

Bio-inspired visual attention models human visual system to detect the most salient part of a visual field. In the existing diversified computational models, bottom-up visual attention that works out a saliency map to indicate the conspicuity of visual stimuli in an image has gained much popularity. This paper introduces a task-driven training procedure into the basic bottom-up computational model to make bio-inspired visual attention more intelligent and appropriate for a particular visual task. Chemical Reaction Optimization (CRO) is a recently proposed evolutionary metaheuristic, simulating the dynamic interaction of molecules in a chemical reaction. In this paper, CRO algorithm is used to optimize the weight coefficients for feature combination through the training procedure. Experimental results show that CRO algorithm outperforms other evolution algorithms in bio-inspired visual attention.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Lu Gan
AU  - Haibin Duan
PY  - 2015
DA  - 2015/06/01
TI  - Chemical Reaction Optimization for Feature Combination in Bio-inspired Visual Attention
JO  - International Journal of Computational Intelligence Systems
SP  - 530
EP  - 538
VL  - 8
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1036220
DO  - 10.1080/18756891.2015.1036220
ID  - Gan2015
ER  -