Graph-based Visual Saliency Model using Background Color

Visual saliency is a cognitive psychology concept that makes some stimuli of a scene stand out relative to their neighbors and attract our attention. Computing visual saliency is a topic of recent interest. Here, we propose a graph-based method for saliency detection, which contains three stages: pre-processing, initial saliency detection, and final saliency detection. The initial saliency map is obtained by putting an adaptive threshold on color differences relative to the background. In final saliency detection, a graph is constructed, and the ranking technique is exploited. In the proposed method, the background is suppressed effectively, and salient regions are often selected correctly. The experimental results on the MSRA-1000 database demonstrate excellent performance and low computational complexity in comparison with the state-of-theart methods.