Diversity Regularized Latent Semantic Match for Hashing

Hashing based approximate nearest neighbors (ANN) search has drawn considerable attraction owing to its low-memory storage and hardware-level logical computing which is doomed to be greatly applicable to quantities of large-scale and practical scenarios, such as information retrieval, computer vision and natural language processing. However, most existing hashing methods concentrate either on images only or on pairwise image-texts (labels, short documents) and rarely utilize more common sentences. In this paper, we propose D iversity R egularized L atent S emantic M atch for H ashing (DRLSMH), a new multimodal hashing method that projects images and sentences into a shared latent semantic space with label-supervised semantic constraints to proceed on multimodal retrieval. Notably, soft orthogonality is induced as a novel regularizer to preserve diverse hashing functions for compact and accurate representations; what's more, this kind of regularization also benefits the derivations of closed-form solutions with some proper relaxations under iterative optimization framework. Extensive experiments on two public datasets demonstrate the advantages of our method over some state-of-the-art baselines under cross-modal retrieval both on image-query-image, image-query-text and text-query-image tasks.