Efficient Discriminative Hashing for Cross-Modal Retrieval

Hashing techniques have been extensively studied in cross-modal retrieval due to their advantages in high computational efficiency and low storage cost. However, existing methods unconsciously ignore the complementary information of multimodal data, thus failing to consider learning discriminative hash codes from the perspective of information complementarity while often involving time-consuming training overhead. To tackle the above issues, we propose an efficient discriminative hashing (EDH) with information complementarity consideration. Specifically, we reckon that multimodal features and their corresponding semantic labels describe heterogeneous data viewed from low-and high-level structures, which owns complementarity. To this end, low-level latent representation and high-level semantics representation are simply derived. Then, a joint learning strategy is formulated to simultaneously exploit the above two representations for generating discriminative hash codes, which is quite computationally efficient. Besides, EDH decomposes hash learning into two steps. To obtain powerful hash functions which are conductive to retrieval, a regularization term considering pairwise semantic similarity is introduced into hash functions learning. In addition, an efficient optimization algorithm is designed to solve the optimization problem in EDH. Extensive experiments conducted on benchmark datasets demonstrate the superiority of our EDH in terms of retrieval performance and training efficiency. The source code is available at https://github.com/hjf-hjf/EDH.