Efficient Object Localization and Segmentation in Weakly Labeled Videos

In this paper, we tackle the problem of efficiently segmenting objects in weakly labeled videos. Internet videos (e.g., YouTube) are often associated with a semantic tag describing the main object within the video. However, this tag does not provide any spatial or temporal information about the object within the video. So these videos are weakly labeled. We propose a novel and efficient approach to localize the object of interest within the video and perform pixel-level segmentation. Given a video with an object tag, our proposed method automatically localizes the object and segments it from the background in each frame of the video. Our method combines object appearance modeling and temporal consistency among frames in a principled framework. Our method does not require user inputs or object detectors, so it can be potentially applied to videos of any object categories. We evaluate our method on a dataset consisting of more than 100 video shots of 10 different object categories. Our experimental results show that our method outperforms other baseline approaches.