A Probabilistic Semantic Model for Image Annotation and Multi-Modal Image Retrieva
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Query-Sensitive Similarity Measure for Content-Based Image Retrieval
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Optimal dimensionality of metric space for classification
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Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Ensembling local learners ThroughMultimodal perturbation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Transfer metric learning by learning task relationships
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning contextual metrics for automatic image annotation
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Multiview Metric Learning with Global Consistency and Local Smoothness
ACM Transactions on Intelligent Systems and Technology (TIST)
Transfer Metric Learning with Semi-Supervised Extension
ACM Transactions on Intelligent Systems and Technology (TIST)
Random forests for metric learning with implicit pairwise position dependence
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Similarity measurement and feature selection using genetic algorithm
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Time series classification by class-specific Mahalanobis distance measures
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Human reidentification with transferred metric learning
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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In many real-world applications, such as image retrieval, it would be natural to measure the distances from one instance to others using instance specific distance which captures the distinctions from the perspective of the concerned instance. However, there is no complete framework for learning instance specific distances since existing methods are incapable of learning such distances for test instance and unlabeled data. In this paper, we propose the Isd method to address this issue. The key of Isd is metric propagation, that is, propagating and adapting metrics of individual labeled examples to individual unlabeled instances. We formulate the problem into a convex optimization framework and derive efficient solutions. Experiments show that Isd can effectively learn instance specific distances for labeled as well as unlabeled instances. The metric propagation scheme can also be used in other scenarios.