Nonparametric saliency detection using kernel density estimation

Abstract

This paper proposes a nonparametric saliency model based on kernel density estimation (KDE) mainly aiming at content-based applications such as salient object segmentation. A set of KDE models are constructed on the basis of regions segmented using the mean shift algorithm. For each pixel, a set of color likelihood measures to all KDE models are calculated, and then the color saliency and spatial saliency of each KDE model are evaluated based on its color distinctiveness and spatial distribution. The final saliency map is generated by combining saliency measures of KDE models and color likelihood measures of pixels. Experimental results demonstrate the better saliency detection performance of our saliency model.