Region based saliency detection by learning background information
Di Chen, Lianyang Ma, Rui Zhang · 2014
Visual saliency detection has become a challenging area in computer vision. In this paper, we propose a novel region based saliency detection model which considers background priors. The proposed method consists of two successive steps - region weighting and contrast computing. In the step of region weighting, we calculate the region weight for each region by region-level image feature and a log-linear prediction model. In the step of contrast computing, we propose a modified contrast computing algorithm by exploiting the advantage of region weights for bottom-up saliency detection. The experimental results on two datasets prove that our method effectively improves the performance on visual saliency detection.