Nonlocal center-surround reconstruction-based bottom-up saliency estimation

The center-surround comparison principle is widely used in existing bottom-up saliency estimation models. However, most of them are based on local image processing techniques which are hard to handle texture regions well as a relatively large neighborhood is required to represent textures. In this paper, we propose a nonlocal patch-based reconstruction approach to reformulate the center-surround comparison. In the proposed approach, the saliency is measured by the reconstruction residual of representing the central patch with a linear combination of its surrounding patches. As a generalization of Itti et al.´s classical center-surround comparison scheme, the proposed approach performs well on images with symmetric structures where Itti et al.´s method fails, as well as on general natural images. Numerical experiments show the proposed approach produces better results compared to the state-of-the-art algorithms on several public databases.