Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Structured metric learning for high dimensional problems
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Semi-supervised distance metric learning for collaborative image retrieval and clustering
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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In a typical content-based image retrieval (CBIR) system, images are represented as vectors and similarities between images are measured by a specified distance metric. However, the traditional Euclidean distance cannot always deliver satisfactory performance, so an effective metric sensible to the input data is desired. Tremendous recent works on metric learning have exhibited promising performance, but most of them suffer from limited label information and expensive training costs. In this paper, we propose two novel metric learning approaches, Optimal Semi-Supervised Metric Learning and its kernelized version. In the proposed approaches, we incorporate information from both labeled and unlabeled data to design a convex and computationally tractable learning framework which results in a globally optimal solution to the target metric of much lower rank than the original data dimension. Experiments on several image benchmarks demonstrate that our approaches lead to consistently better distance metrics than the state-of-the-arts in terms of accuracy for image retrieval.