Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large Margin Nearest Neighbor Classifiers
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Adaptive Kernel Metric Nearest Neighbor Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
Object of interest-based visual navigation, retrieval, and semantic content identification system
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Semi-supervised sparse metric learning using alternating linearization optimization
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
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In this paper, the authors propose a kernel-based approach to improve the retrieval performances of CBIR systems by learning a distance metric based on class probability distributions. Unlike other metric learning methods which are based on local or global constraints, the proposed method learns for each class a nonlinear kernel which transforms the original feature space to a more effective one. The distances between query and database images are then measured in the new space. Experimental results show that the kernel-based approach not only improves the retrieval performances of kernel distance without learning, but also outperforms other kernel metric learning methods.