Saliency and depth-based unsupervised object segmentation

摘要

This study demonstrates an unsupervised segmentation algorithm for video sequences acquired from a moving camera with results comparable to semi-supervised (interactive) methods. The authors employ depth cues from multiple views stereo to enhance the hypothesis of a potential object based on saliency scores. The resulting object and background hypotheses are then used to model foreground and background distributions for a graph-cut-based segmentation. The authors' graph-cut framework simultaneously optimises over depth and colour information to produce automatically segmented objects in challenging unstructured scenes. They refer to this saliency and depth-based segmentation method as `SDCut'. The proposed method is fully automatic without requiring any intervention. Experiments demonstrate that their method can achieve accurate segmentation results which are comparable with several well-known human interactive semi-supervised segmentation methods.