DAH: Discrete Asymmetric Hashing for Efficient Cross-Media Retrieval

Abstract

Given the merits in high computational efficiency and low storage cost, hashing techniques have been widely studied in cross-media retrieval. Existing methods usually adopt the equal length encoding scheme to represent the multimedia data. However, the strictly equal length scheme maybe not optimal because the dimension of different modalities is often various. Besides, there exists other challenges in designing a cross-media retrieval system, e.g., how to address the discrete constraints, how to avoid using the n*n similarity matrix, and how to effectively exploit the discriminative label information. To conquer the above challenges, we propose a novel method, i.e., discrete asymmetric hashing (DAH). Specifically, DAH exploits a flexible model, which can seamlessly deal with equal or unequal length encoding scenarios. Moreover, DAH constructs a supervised semantic embedding framework by jointly minimizing the distance-distance difference and label reconstructing error, significantly reducing the computational complexity. An asymmetric strategy is employed to establish the connection between hash codes and the latent subspace. Furthermore, the hash codes can be learned discretely by the designed optimization algorithm. In the training stage2, a semantic intersection scheme is proposed to learn more powerful hash functions. Experiments show that our DAH is effective in equal and unequal scenarios.