Saliency and depth‐based unsupervised object segmentation
Hu He · IET Image Processing · 2016
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.