Saliency Detection Using Color Spatial Variance Weighted Graph Model

Abstract

Saliency detection as a recently active research field of computer vision, has a wide range of applications, such as pattern recognition, image retrieval, adaptive compression, target detection, etc. In this paper, we propose a saliency detection method based on color spatial variance weighted graph model, which is designed rely on a background prior. First, the original image is partitioned into small patches, then we use mean-shift clustering algorithm on this patches to get sorts of clustering centers that represents the main colors of whole image. In modeling stage, all patches and the clustering centers are denoted as nodes on a specific graph model. The saliency of each patch is defined as weighted sum of weights on shortest paths from the patch to all clustering centers, each shortest path is weighted according to color spatial variance. Our saliency detection method is computational efficient and outperformed the state of art methods by higher precision and better recall rates, when we took evaluation on the popular MSRA1000 database.