We propose a novel approach for object co-segmentation in arbitrary videos by sampling, tracking and matching object proposals via a Regulated Maximum Weight Clique (RMWC) extraction scheme. The proposed approach is able to achieve good segmentation results by pruning away noisy segments in each video through selection of object proposal tracklets that are spatially salient and temporally consistent, and by iteratively extracting weighted groupings of objects with similar shape and appearance (with-in and across videos). The object regions obtained from the video sets are used to initialize per-pixel segmentation to get the final co-segmentation results. Our approach is general in the sense that it can handle multiple objects, temporary occlusions, and objects going in and out of view. Additionally, it makes no prior assumption on the commonality of objects in the video collection. The proposed method is evaluated on publicly available multi-class video object co-segmentation dataset and demonstrates improved performance compared to the state-of-the-art methods.
ECCV 2014 PaperVideoObjectCoSegmentation.pdf
We have collected a challenging video co-segmentation dataset, which consists of 9 videos. There are 5 types of animals appear in these videos, and the goal is to segment these animals from the videos and assign same label for same type of animal.Safari_1.0.zip