Saliency prediction by Mahalanobis distance of topological feature on deep color components

Abstract

A new saliency prediction method via extracting topological feature and calculating Mahalanobis distance on deep color components is presented in this paper. Specifically, four selectable schemes of color components are considered and a deep convolutional network is used to learn the best scheme. Then the topological feature maps of an input image are extracted on the learned color components by the analysis of connectivity and adjacency. To achieve the final saliency map, a new fusion method is proposed by calculating the Mahalanobis distance between the feature maps and their means with their covariance matrices rather than summating the feature maps linearly. The numerical and visual evaluation shows that a competitive performance compared with fourteen state-of-the-art models is achieved by the proposed method.