Supervised discrete cross-modal hashing based on kernel discriminant analysis. (February 2020) - Fang, Yixian; Ren, Yuwei
Highlights: We propose a supervised discrete cross-modal hashing framework which can establish strong and effective connection between different modalities and preserve the discrete constraint, thus reducing the quantization loss. A compact optimization strategy is presented to directly learn the hash codes in a closed form, rather than bit by bit. The evaluation on four real-world datasets demonstrates the superior performance of SDCH-KDA over the state-of-the-arts methods. Especially on the LabelMe dataset, SDCH-KDA promotes an average of 9% improvement compared to the best results available. Abstract: Cross-modal hashing methods have drawn considerable attention due to the rapid growth of multi-modal data. To obtain efficient binary codes in a low-dimensional Hamming space, most existing approaches relaxed the discrete constraint, which could cause quantization loss and even result in performance degradation. In order to avoid this bottleneck, some scholars employed iterative discrete cyclic coordinate descent (DCC) to learn hash codes bit by bit, but this was very time-consuming. To counter this problem, a simple yet novel supervised discrete cross-modal hashing framework is represented to directly learn the unified discrete binary codes with a close-form, rather than bit by bit. Furthermore, to preserve label separability, the kernel discriminant analysis is fused into the proposed framework to enrich the discrimination ability of the learned binary codes. The goal of the proposed method is to obtain the common discrete binary codes of different modalities in a shared latent Hamming space so that the different modalities of a sample can be effectively connected. Experimental study shows the encouraging results of the proposed algorithm in comparisons to the state-of-the-art baseline approaches on four real-world datasets. Especially on the LabelMe dataset, the superiority of the proposed method is obvious, with an average improvement of 9% over the best available results.