HOSO: Histogram of Surface Orientation for RGB-D Salient Object Detection

Abstract

Salient object detection using RGB-D data is an emerging field in computer vision. Salient regions are often characterized by an unusual surface orientation profile with respect to the surroundings. To capture such profile, we introduce the histogram of surface orientation (HOSO) feature to measure surface orientation distribution contrast for RGB-D saliency. We propose a new unified model that integrates surface orientation distribution contrast with depth and color contrast across multiple scales. This model is implemented in a multi-stage saliency computation approach that performs contrast estimation using a kernel density estimator (KDE), estimates object positions from the low-level saliency map, and finally refines the estimated object positions with a graph cut based approach. Our method is evaluated on two RGB-D salient object detection databases, achieving superior performance to previous state-of-the-art methods.