Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
ACM Computing Surveys (CSUR)
Feature Extraction and a Database Strategy for Video Fingerprinting
VISUAL '02 Proceedings of the 5th International Conference on Recent Advances in Visual Information Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Metric Learning for Text Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally adaptive subspace and similarity metric learning for visual data clustering and retrieval
Computer Vision and Image Understanding
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
A unified framework of subspace and distance metric learning for face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Learning distance functions for image retrieval
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Modulation-scale analysis for content identification
IEEE Transactions on Signal Processing - Part II
Classification and Feature Extraction by Simplexization
IEEE Transactions on Information Forensics and Security
Spatio–Temporal Transform Based Video Hashing
IEEE Transactions on Multimedia
Robust Video Fingerprinting for Content-Based Video Identification
IEEE Transactions on Circuits and Systems for Video Technology
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This paper considers a distance metric learning (DML) algorithm for a fingerprinting system, which identifies a query content by finding the fingerprint in the database (DB) that measures the shortest distance to the query fingerprint. For a given training set consisting of original and distorted fingerprints, a distance metric equivalent to the lp norm of the difference of two linearly projected fingerprints is learned by minimizing the false-positive rate (probability of perceptually dissimilar content to be identified as being similar) for a given false-negative rate (probability of perceptually similar content to be identified as being dissimilar). The learned metric can perform better than the often used lp distance and improve the robustness against a set of unexpected distortions. In the experiments, the distance metric learned by the proposed algorithm performed better than those metrics learned by well-known DML algorithms for classification.