Optimal semi-supervised metric learning for image retrieval

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.