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

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.