Metric learning via feature weighting for scalable image retrieval

Two dominant image retrieval schemes are based on local features indexed by an inverted index and global features indexed by compact hashing codes. They both demonstrate excellent scalability, but distinct strength for image retrieval. This motivates us to investigate how to fuse these two search schemes, to further enhance the retrieval effectiveness. Thus, we propose a novel metric learning method, namely Metric Learning via Feature Weighting (MLFW), to effectively fuse different features. MLFW learns the distance metric on individual feature as well as the weights of different features in a joint framework, to combine the learned distance obtained from all the individual feature and the early fusion. Furthermore, we design an efficient solution to optimize the objective function. Extensive experimental results conducted on real-life datasets show that the proposed MLFW outperforms the state-of-the-art methods in terms of search quality.