Semantic Co segmentation in Videos
Discovering and segmenting objects in videos is a challenging task due to large variations of objects in appearances, deformed shapes and cluttered backgrounds. In this paper, we propose to segment objects and understand their visual semantics from a collection of videos that link to each other, which we refer to as semantic co segmentation. Without any prior knowledge on videos, we first extract semantic objects and utilize a tracking based approach to generate multiple object like tracklets across the video. Each tracklet maintains temporally connected segments and is associated with a predicted category. To exploit rich information from other videos, we collect tracklets that are assigned to the same category from all videos, and co select tracklets that belong to true objects by solving a submodular function. This function accounts for object properties such as appearances, shapes and motions, and hence facilitates the co segmentation process. Experiments on three video object segmentation datasets show that the proposed algorithm performs favorably against the other state of the art methods.
ECCV