SDSP: A novel saliency detection method by combining simple priors

Abstract

Salient regions detection from images is an important and fundamental research problem in neuroscience and psychology and it serves as an indispensible step for numerous machine vision tasks. In this paper, we propose a novel conceptually simple salient region detection method, namely SDSP, by combining three simple priors. At first, the behavior that the human visual system detects salient objects in a visual scene can be well modeled by band-pass filtering. Secondly, people are more likely to pay their attention on the center of an image. Thirdly, warm colors are more attractive to people than cold colors are. Extensive experiments conducted on the benchmark dataset indicate that SDSP could outperform the other state-of-the-art algorithms by yielding higher saliency prediction accuracy. Moreover, SDSP has a quite low computational complexity, rendering it an outstanding candidate for time critical applications. The Matlab source code of SDSP and the evaluation results have been made online available at http://sse.tongji.edu.cn/linzhang/va/SDSP/SDSP.htm.