Video Object Segmentation by Hierarchical Localized Classification of Regions

Abstract

Video Object Segmentation (VOS) is to cut out a selected object from video sequences, where the main difficulties are shape deformation, appearance variations and background clutter. To cope with these difficulties, we propose a novel method, named as Hierarchical Localized Classification of Regions (HLCR). We suggest that appearance models as well as the spatial and temporal coherence between frames are the keys to break through bottleneck. Locally, in order to identify foreground regions, we propose to use Hierarchial Localized Classifiers, which organize regional features as decision trees. In global, we adopt Gaussian Mixture Color Models (GMMs). After integrating the local and global results into a probability mask, we can achieve the final segmentation result by graph cut. Experiments on various challenging video sequences demonstrate the efficiency and adaptability of the proposed method.