关键词: 全局对比, 背景先验, 超像素分割, 显著目标

Abstract: In order to overcome the problems of imprecise in background extraction and weak ability of anti-background existed in traditional approaches of background priors, a salient object detection method using the global contrast and background priors is proposed in this paper. Firstly, the source image is segmented into a series of perceptually uniform superpixels, a global-based saliency map is then calculated by contrasting global color and the foreground seed is collected. Secondly, those superpixels with large difference are selected as the background seeds by comparing each boundary superpixels with the foreground seeds, and the background-based map is then computed. Finally, based on the integration of background-based and global-based saliency map, a multi foci of interest Gaussian model is proposed to reduce background and highlight salient region. Compared with 6 state-of the-art methods on publicly available benchmark datasets（MSRA-1000）, the simulation results demonstrate that the salient object detection approach proposed in this paper performs more robustly in dealing with the complex boundary information and suppressing noise comparing with 6 conventional methods.

Key words: global contrast, background priors, superpixel segmentation, salient object