An effective regional saliency model based on extended site entropy rate

In this paper, we propose a new region-based saliency model to simulate the human visual attention. First, we construct a pixel-level fully-connected graph representation for an image, and perform normalized cut to segment the image based on the proximity and similarity principles. After obtaining image regions, we reconstruct a region-based fully-connected graph. Based on the saliency principle “center-surround contrast”, we define new dissimilarity functions in terms of several visual features. Finally we run a random walk on the region graph and apply site entropy rate to measure the region saliency. We evaluate the proposed model on a public dataset consisting of 120 images. Experimental results demonstrate that our model predicts eye fixations more accurately than the other four state-of-the-art methods. We also apply our saliency model to improve the performance of image retargeting.