Fusion of Color-Based Multi-Dimensional Scaling Maps For Saliency Estimation

Abstract

This work presents an original energy-based model, using a pixel pair modeling combined with a fusion procedure, to the saliency map estimation problem. More precisely, we formulate the saliency map segmentation issue as the solution of an energy-based model involving pixel pairwise constraints, in terms of color features, to which are then added constraints of higher levels of abstraction given by a preliminary over-segmentation whose location of regions but also contour information are exploited. Finally, this segmentation-driven saliency measure solution is then expressed in different color spaces which are combined together in order to take into account the specific properties of each of these color models with a outlier rejection scheme. Experimental results show that the proposed algorithm is both simple, efficient by performing favorably against state-of-the-art methods and also perfectible.