The nature of statistical learning theory
The nature of statistical learning theory
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Weakly Supervised Object Localization with Stable Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
International Journal of Computer Vision
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
Cutting-plane training of structural SVMs
Machine Learning
ClassCut for unsupervised class segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Category independent object proposals
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In the class based image segmentation problem, one of the major concerns is to provide large training data for learning complex graphical models. To alleviate the labeling effort, a concise annotation approach working on bounding boxes is introduced. The main idea is to leverage the knowledge learned from a few object contours for the inference of unknown contours in bounding boxes. To this end, we incorporate the bounding box prior into the concept of multiple image segmentations to generate a set of distinctive tight segments, with the condition that at least one tight segment approaching to the true object contour. A good tight segment is then selected via semi-supervised regression, which bears the augmented knowledge transferred from object contours to bounding boxes. The experimental results on the challenging Pascal VOC dataset corroborate that our new annotation method can potentially replace the manual annotations.