A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
ACM SIGGRAPH 2005 Papers
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Optimal contour closure by superpixel grouping
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Unsupervised co-segmentation through region matching
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Key-segments for video object segmentation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi-index | 0.00 |
Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos.