Semantic object segmentation via detection in weakly labeled video

Yu Zhang, Xiaowu Chen, Jia Li, Chen Wang, Changqun Xia
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
Semantic object segmentation in video is an important step for large-scale multimedia analysis. In many cases, however, semantic objects are only tagged at video-level, making them difficult to be located and segmented. To address this problem, this paper proposes an approach to segment semantic objects in weakly labeled video via object detection. In our approach, a novel video segmentationby-detection framework is proposed, which first incorporates object and region detectors pre-trained on
more » ... ill images to generate a set of detection and segmentation proposals. Based on the noisy proposals, several object tracks are then initialized by solving a joint binary optimization problem with min-cost flow. As such tracks actually provide rough configurations of semantic objects, we thus refine the object segmentation while preserving the spatiotemporal consistency by inferring the shape likelihoods of pixels from the statistical information of tracks. Experimental results on Youtube-Objects dataset and SegTrack v2 dataset demonstrate that our method outperforms state-of-the-arts and shows impressive results.
doi:10.1109/cvpr.2015.7298987 dblp:conf/cvpr/ZhangCLWX15 fatcat:65bkleamgza3hah5m5ujdq5dsm