Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ACM SIGGRAPH 2005 Papers
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
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
A Framework for Image Segmentation Using Shape Models and Kernel Space Shape Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting Moving People from Internet Videos
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data-driven crowd analysis in videos
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We present an online Web-data-driven framework for segmenting moving objects in videos. This framework uses object shape priors learned in an online fashion from relevant labeled images ranked in a large-scale Web image set. The method for online prior learning has three steps: (1) relevant silhouette images for training are online selected using a user-provided bounding box and an object class annotation; (2) image patches containing the annotated object for testing are obtained via an online trained tracker; (3) a holistic shape energy term is learned for the object, while the object and background seed labels are propagated between frames. Finally, the segmentation is optimized via 3-D Graph cuts with the shape term and soft assignments of seeds. The system's performance is evaluated on the challenging Youtube dataset and found to be competitive with the state-of-the-art that requires offline modeling based on pre-selected templates and a pre-trained person detector. Comparison experiments have verified that tracking and seed label propagation both induce less distraction, while the shape prior induces more complete segments.