A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Invariant Contour Completion Using Conditional Random Fields
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Boundary Extraction in Natural Images Using Ultrametric Contour Maps
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Maximum expected F-measure training of logistic regression models
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Discriminative Sparse Image Models for Class-Specific Edge Detection and Image Interpretation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Multi-scale Improves Boundary Detection in Natural Images
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Figure/Ground assignment in natural images
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Learning-Based symmetry detection in natural images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Multi-frequency transformation for edge detection
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
SLEDGE: Sequential Labeling of Image Edges for Boundary Detection
International Journal of Computer Vision
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In this work we propose a boosting-based approach to boundary detection that advances the current state-of-the-art. To achieve this we introduce the following novel ideas: (a) we use a training criterion that approximates the F-measure of the classifier, instead of the exponential loss that is commonly used in boosting. We optimize this criterion using Anyboost. (b) We deal with the ambiguous information about orientation of the boundary in the annotation by treating it as a hidden variable, and train our classifier using Multiple-Instance Learning. (c) We adapt the Filterboost approach of [1] to leverage information from the whole training set to train our classifier, instead of using a fixed subset of points. (d) We extract discriminative features from appearance descriptors that are computed densely over the image. We demonstrate the performance of our approach on the Berkeley Segmentation Benchmark.