Saliency Detection Using Region-Based Incremental Center-Surround Distance

Abstract

A new method to detect salient region(s) in images is proposed in this paper. The proposed approach, which is inspired by object-based visual attention theory, segments the input image into coherent regions and measures region-based center-surround distance (RBCSD), which is a distance between region attributes such as color histograms found in each region and its surrounding region. Furthermore, segmented regions are merged such that the RBCSD of the merged region is greater than the individual RBCSD of the component regions through region-based incremental center surround distance (RBCSD+I) process. Due to this RBCSD+I process, merged regions may contain incoherent color regions, which improves the robustness of the proposed approach. The key advantages of the proposed algorithm are: (1) it provides a salient region with plausible object boundaries, (2) it is robust to color incoherency present in the salient region, and (3) it is computationally efficient. Extensive qualitative and quantitative evaluation of the proposed algorithm on widely used data sets and comparison with the existing saliency detection approaches clearly indicates the feasibility and efficiency of the proposed approach.