Saliency detection based on local contrast and global rarity
HE Liang-ji · Jisuanji yingyong yanjiu · 2014
In order to improve the accuracy of saliency map and extract salient object more precisely,this paper proposed a algorithm combined local contrast and global rarities. The algorithm measured saliency by two measures: local contrast and global rarity. It simulated‘center-surround difference'with multi-scale difference of Gaussian to calculate the difference between features and surrounding features to get local contrast. By using the probability of features and features' variance of multi-scale Gaussian convoluted image,global rarity of each pixel could be described. It fused and normalized the local contrast and global rarity to get the final integrated saliency map. Experimental results show that this method can detect salient regions in an image,it can not only highlight the edge of different object in images,but also highlight the saliency of homogeneous region in image. It shows that model which combine local and global saliency can get better saliency map.