Multi-scale Feature Fusion for 3D Saliency Detection

Abstract

Abstract 3D saliency detection aims to take advantage of the disparity map, depth map and color information to automatically detect informative objects from natural scenes. Although studies have concentrated on this issue in recent years, there are challenges such as how to leverage disparity map or depth map effectively to compute depth-induced saliency, and how to fuse optimally multiple visual features and cues. A novel 3D saliency detection approach is proposed, which fuses local contrast, region contrast, texture feature, depth cue, and location cue into a unified saliency computation framework. Results show that the proposed approach achieves significant and consistent improvements on other advanced methods in the RGBD1000 datasets.