Visual saliency based on selective integration of feature maps in frequency domain

Abstract

In this paper, an automatic method for extracting visual saliency based on selective integration of feature maps in frequency domain is proposed. Feature maps are calculated by measuring the Bayes spectral entropy. In order to extract visual saliency effectively, feature maps are first generated from three images separated into Y, Cb, Cr channels, respectively. Then, by selectively integrating feature maps, visual saliency is finally extracted. Experimental results have shown that the proposed method obtains good performance of visual saliency under various environments containing multiple objects and cluttered backgrounds in natural images.