A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Linear Programming Boosting via Column Generation
Machine Learning
Convex Optimization
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
The Journal of Machine Learning Research
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
A boosted classifier tree for hand shape detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Cosine similarity metric learning for face verification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Similarity scores based on background samples
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
A scalable dual approach to semidefinite metric learning
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
An associate-predict model for face recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Robust and efficient regularized boosting using total Bregman divergence
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Shape Retrieval Using Hierarchical Total Bregman Soft Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A proper distance metric is fundamental in many computer vision and pattern recognition applications such as classification, image retrieval, face recognition and so on. However, it is usually not clear what metric is appropriate for specific applications, therefore it becomes more reliable to learn a task oriented metric. Over the years, many metric learning approaches have been reported in literature. A typical one is to learn a Mahalanobis distance which is parameterized by a positive semidefinite (PSD) matrix M. An efficient method of estimating M is to treat M as a linear combination of rank-one matrices that can be learned using a boosting type approach. However, such approaches have two main drawbacks. First, the weight change across the training samples may be non-smooth. Second, the learned rank-one matrices might be redundant. In this paper, we propose a doubly regularized metric learning algorithm, termed by DRMetric, which imposes two regularizations on the conventional metric learning method. First, a regularization is applied on the weight of the training examples, which prevents unstable change of the weights and also prevents outlier examples from being weighed too much. Besides, a regularization is applied on the rank-one matrices to make them independent. This greatly reduces the redundancy of the rank-one matrices. We present experiments depicting the performance of the proposed method on a variety of datasets for various applications.