Hashing plays an important role in information retrieval, due to its low storage and high speed of processing. Among the techniques available in the literature, multi-modal hashing, which can encode heterogeneous multi-modal features into compact hash codes, has received particular attention. Most of the existing multi-modal hashing methods adopt the fixed weighting factors to fuse multiple modalities for any query data, which cannot capture the variation of different queries. Besides, many methods introduce hyper-parameters to balance many regularization terms that make the optimization harder. Meanwhile, it is time-consuming and labor-intensive to set proper parameter values. The limitations may significantly hinder their promotion in real applications. In this paper, we propose a simple, yet effective method that is inspired by the Hadamard matrix. The proposed method captures the multi-modal feature information in an adaptive manner and preserves the discriminative semantic information in the hash codes. Our framework is flexible and involves a very few hyper-parameters. Extensive experimental results show the method is effective and achieves superior performance compared to state-of-the-art algorithms.

翻译：在文献中现有的技术中,多式散列可以将多种多式特征编码为紧凑散列码,受到特别关注。大多数现有的多式散列方法都采用固定加权因素,以整合任何查询数据的多种模式,无法捕捉不同查询的变异。此外,许多方法都采用超参数来平衡许多正规化条件,使优化工作更难进行。与此同时,制定适当的参数值需要时间和劳力密集型。这些限制可能大大妨碍这些参数在实际应用中的推广。在本文件中,我们提出了一个由哈达马德矩阵启发的简单而有效的方法。拟议方法以适应的方式捕捉到多式特征信息,并保存在散列码中带有歧视性的语义信息。我们的框架灵活灵活,涉及极少的超参数。广泛的实验结果显示,这种方法是有效的,而且比状态算法效果优。