Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Grouping with Directed Relationships
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Automatically Labeling Video Data Using Multi-class Active Learning
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Naming every individual in news video monologues
Proceedings of the 12th annual ACM international conference on Multimedia
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tools for protecting the privacy of specific individuals in video
EURASIP Journal on Applied Signal Processing
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Semi-supervised graph clustering: a kernel approach
Machine Learning
Breast cancer identification: KDD CUP winner's report
ACM SIGKDD Explorations Newsletter
Bagging Constraint Score for feature selection with pairwise constraints
Pattern Recognition
Medical data mining: insights from winning two competitions
Data Mining and Knowledge Discovery
Similarity beyond distance measurement
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
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In video object classification, insufficient labeled data may at times be easily augmented with pairwise constraints on sample points, i.e, whether they are in the same class or not. In this paper, we proposed a discriminative learning approach which incorporates pairwise constraints into a conventional margin-based learning framework. The proposed approach offers several advantages over existing approaches dealing with pairwise constraints. First, as opposed to learning distance metrics, the new approach derives its classification power by directly modeling the decision boundary. Second, most previous work handles labeled data by converting them to pairwise constraints and thus leads to much more computation. The proposed approach can handle pairwise constraints together with labeled data so that the computation is greatly reduced. The proposed approach is evaluated on a people classification task with two surveillance video datasets.