Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Particle Video: Long-Range Motion Estimation using Point Trajectories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Moving Object Segmentation Using Optical Flow and Depth Information
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Enhanced Local Subspace Affinity for feature-based motion segmentation
Pattern Recognition
Dense point trajectories by GPU-accelerated large displacement optical flow
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Object segmentation by long term analysis of point trajectories
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Detecting the Presence of Stationary Objects from Sparse Stereo Disparity Space
PSIVT '10 Proceedings of the 2010 Fourth Pacific-Rim Symposium on Image and Video Technology
A Video Database for the Development of Stereo-3D Post-Production Algorithms
CVMP '10 Proceedings of the 2010 Conference on Visual Media Production
Scene Segmentation Assisted by Stereo Vision
3DIMPVT '11 Proceedings of the 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission
Motion segmentation by model-based clustering of incomplete trajectories
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Track to the future: Spatio-temporal video segmentation with long-range motion cues
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Two-granularity tracking: mediating trajectory and detection graphs for tracking under occlusions
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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Motion-based video segmentation has been studied for many years and remains challenging. Ill-posed problems must be solved when seeking for a fully automated solution, so it is increasingly popular to maintain users in the processing loop by letting them set parameters or draw mattes to guide the segmentation process. When processing multiple-view videos, however, the amount of user interaction should not be proportional to the number of views. In this paper we present a novel sparse segmentation algorithm for two-view stereoscopic videos that maintains temporal coherence and view consistency throughout. We track feature points on both views with a generic tracker and analyse the pairwise affinity of both temporally overlapping and disjoint tracks, whereas existing similar techniques only exploit the information available when tracks overlap. The use of stereo-disparity also allows our technique to process jointly feature tracks on both views, exhibiting a good view consistency in the segmentation output. To make up for the lack of high level understanding inherent to segmentation techniques, we allow the user to refine the output with a split-and-merge approach so as to obtain a desired view-consistent segmentation output over many frames in a few clicks. We present several real video examples to illustrate the versatility of our technique.