Weakly supervised object localization and segmentation in videos.

We consider the problem of localizing and segmenting objects in weakly labeled video. A video is weakly labeled if it is associated with a tag (e.g. YouTube videos with tags) describing the main object present in the video. It is weakly labeled because the tag only indicates the presence/absence of the object, but does not give the detailed spatial/temporal location of the object in the video. Given a weakly labeled video, our method can automatically localize the object in each frame and segment it from the background. Our method is fully automatic and does not require any user-input. In principle, it can be applied to a video of any object class. We evaluate our proposed method on a dataset with more than 100 video shots. Our experimental results show that our method outperforms other baseline approaches. A method to localize and segment objects in weakly labeled videos is proposed.The method relies on object appearance model and temporal consistency constraint.Chain structured graphical model formulation is used to localize an object.The method does not require labeled training data.The method is fully automatic and does not require any user interaction.