Saliency Detection Based on Weighted Saliency Probability

Abstract

The key to computer-based image recognition is to distinguish salient objects from the image background. However, it is still challenging to detect salient region when an object significantly touches the image boundaries. In this study, we present a novel salient region detection method based on a color space volume and a novel weighted saliency probability to address the issue. First, we propose a novel color space volume, and it was regarded as the foreground based on the L^*a^*b^* color space. Second, we present a new background measure called background probability to find background regions by exploiting a background prior and centroid distance weights. Moreover, we propose a new foreground measure called foreground probability to detect foreground by utilizing the brightness of color space volume. Finally, we propose a novel weighted saliency probability to obtain a clean and uniform salient map based on the background probability and the foreground probability. Experiments on three benchmark image datasets demonstrated that the proposed method outperformed several well-known saliency detection methods.