A Computational Model for Saliency Maps by Using Local Entropy

Abstract

This paper presents a computational framework for saliency maps. It employs the Earth Mover's Distance based on weighted-Histogram (EMD-wH) to measure the center-surround difference, instead of the Difference-of-Gaussian (DoG) filter used by traditional models. In addition, the model employs not only the traditional features such as colors, intensity and orientation but also the local entropy which expresses the local complexity. The major advantage of combining the local entropy map is that it can detect the salient regions which are not complex regions. Also, it uses a general framework to integrate the feature dimensions instead of summing the features directly. This model considers both local and global salient information, in contrast to the existing models that consider only one or the other. Furthermore, the "large scale bias" and "central bias" hypotheses are used in this model to select the fixation locations in the saliency map of different scales. The performance of this model is assessed by comparing their saliency maps and human fixation density. The results from this model are finally compared to those from other bottom-up models for reference.