Abstract
Visual saliency detection provides an alternative methodology to semantic image understanding in many applications such as region-based image retrieval and adaptive compression of images. In this paper, we propose an approach which utilizes both global and local cues to extract saliency information. Our method can achieve better performance than existing saliency detection methods in terms of precision and recall rates. The main contributions are threefold: 1) a new model which can better describe the color perception of human beings is proposed. Based on this model, a global color contrast cue is also presented. 2) as supplements, two other global cues and one local cues are also presented to capture as much saliency information as we can. 3) a CRF model is used to integrate these cues and generate the final saliency map. Experimental results indicate that our proposed approach is effective and practicable.