Robust Supervised Deep Discrete Hashing for Cross-Modal Retrieval

The exponential growth of multi-modal data in the real world poses significant challenges to efficient retrieval, and traditional single-modal methods are no longer suitable for the growth of multi-modal data. To address this issue, hashing retrieval methods play an important role in cross-modal retrieval tasks when referring to a large amount of multi-modal data. However, effectively embedding multi-modal data into a common low-dimensional Hamming space remains challenging. A critical issue is that feature redundancies in existing methods lead to suboptimal hash codes, severely degrading retrieval performance; yet, selecting optimal features remains an open problem in deep cross-modal hashing. In this paper, we propose an end-to-end approach, named Robust Supervised Deep Discrete Hashing (RSDDH), which can accomplish feature learning and hashing learning simultaneously. RSDDH has a hybrid deep architecture consisting of a convolutional neural network and a multilayer perceptron adaptively learning modality-specific representations. Moreover, it utilizes a non-redundant feature selection strategy to select optimal features for generating discriminative hash codes. Furthermore, it employs a direct discrete hashing scheme (SVDDH) to solve the binary constraint optimization problem without relaxation, fully preserving the intrinsic properties of hash codes. Additionally, RSDDH employs inter-modal and intra-modal consistency preservation strategies to reduce the gap between modalities and improve the discriminability of learned Hamming space. Extensive experiments on four benchmark datasets demonstrate that RSDDH significantly outperforms state-of-the-art cross-modal hashing methods.