We present a video co-segmentation method that uses category-independent object proposals as its basic element and can extract multiple foreground objects in a video set. The use of object elements overcomes limitations of low-level feature representations in separating complex foregrounds and backgrounds. We formulate object-based co-segmentation as a co-selection graph in which regions with foreground-like characteristics are favored while also accounting for intra-video and inter-video foreground coherence. To handle multiple foreground objects, we expand the co-selection graph model into a proposed multi-state selection graph model (MSG) that optimizes the segmentations of different objects jointly. This extension into the MSG can be applied not only to our co-selection graph, but also can be used to turn any standard graph model into a multi-state selection solution that can be optimized directly by the existing energy minimization techniques. Our experiments show that our object-based multiple foreground video co-segmentation method (ObMiC) compares well to related techniques on both single and multiple foreground cases.
There are two datasets used in our paper: MOViCS dataset and our Video Coseg dataset.
 "Object-based Multiple Foreground Video Co-segmentation"
Huazhu Fu, Dong Xu, Bao Zhang, Stephen Lin,
in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 3166-3173.
 "Object-based Multiple
Foreground Video Co-segmentation via Multi-state Selection Graph"
Huazhu Fu, Dong Xu, Bao Zhang, Stephen Lin, Rabab K. Ward,
IEEE Transactions on Image Processing (TIP), vol. 24, no. 11, pp. 3415-3424, 2015.
The code can be found from here:
Our Dataset and Groundtruth (~5MB) has 8 videos (2 video in each group) including 2 objects in each video. Download: [OneDrive] [BaiduYun]
Other related video co-segmentation dataset: MOViCS (CVPR13) [Project Link] .
 Huazhu Fu, Xiaochun Cao, Zhuowen Tu, "Cluster-based Co-saliency Detection", IEEE
Transactions on Image Processing (TIP), vol. 22, no. 10, pp. 3766-3778, 2013.
 Xiaochun Cao, Zhiqiang Tao, Bao Zhang, Huazhu Fu, Wei Feng, "Self-adaptively Weighted Co-saliency Detection via Rank Constraint", IEEE Transactions on Image Processing (TIP), vol. 23, no. 9, pp. 4175-4186, 2014. [PDF] [Code]
 Huazhu Fu, Dong Xu, Stephen Lin, Jiang Liu, "Object-based RGBD Image Co-segmentation with Mutex Constraint", in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4428-4436. [PDF] [Project]