Salient region detection Using Wasserstein distance measure based on nonlinear scale space

Abstract

Many existing bottom-up saliency detection methods measure the multi-scale local prominence by building the Gaussian scale space. As a kind of linear scale space, it is a natural representation of human perception. However the Gaussian filtering does not respect the boundaries of proto-objects and smooth both noises and details. In this paper, we compute the pixel level center-surround difference in a nonlinear scale space which makes blurring locally adaptive to the image regions. The nonlinear scale space is built by a efficient evolution techniques and extended to represent color images. In contrast to some widely used region-based measures, we represent feature statistics by multivariate normal distributions and compare them with the Wasserstein distance on l 2 norm ( W 2 distance). From the perspective of visual organization in imaging, many priors are proved to be efficient in global consideration. In order to further precisely locate the proper salient object, we also use the background prior as a global cue to refine the obtained local saliency map. The experimental results show that our approach outperforms 5 recent state of the art saliency detection methods in terms of precision and recall on a newly published benchmark.