Online Asymmetric Similarity Learning for Cross-Modal Retrieval

Cross-modal retrieval has attracted intensive attention in recent years. Measuring the semantic similarity between heterogeneous data objects is an essential yet challenging problem in cross-modal retrieval. In this paper, we propose an online learning method to learn the similarity function between heterogeneous modalities by preserving the relative similarity in the training data, which is modeled as a set of bi-directional hinge loss constraints on the cross-modal training triplets. The overall online similarity function learning problem is optimized by the margin based Passive-Aggressive algorithm. We further extend the approach to learn similarity function in reproducing kernel Hilbert spaces by kernelizing the approach and combining multiple kernels derived from different layers of the CNN features using the Hedging algorithm. Theoretical mistake bounds are given for our methods. Experiments conducted on real world datasets well demonstrate the effectiveness of our methods.