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Unsupervised multi-graph cross-modal hashing for large-scale multimedia retrieval

机译：用于大型多媒体检索的无监督多图交叉模态哈希

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摘要

With the advance of internet and multimedia technologies, large-scale multi-modal representation techniques such as cross-modal hashing, are increasingly demanded for multimedia retrieval. In cross-modal hashing, three essential problems should be seriously considered. The first is that effective cross-modal relationship should be learned from training data with scarce label information. The second is that appropriate weights should be assigned for different modalities to reflect their importance. The last is the scalability of training process which is usually ignored by previous methods. In this paper, we propose Multi-graph Cross-modal Hashing (MGCMH) by comprehensively considering these three points. MGCMH is unsupervised method which integrates multi-graph learning and hash function learning into a joint framework, to learn unified hash space for all modalities. In MGCMH, different modalities are assigned with proper weights for the generation of multi-graph and hash codes respectively. As a result, more precise cross-modal relationship can be preserved in the hash space. Then Nystrom approximation approach is leveraged to efficiently construct the graphs. Finally an alternating learning algorithm is proposed to jointly optimize the modality weights, hash codes and functions. Experiments conducted on two real-world multi-modal datasets demonstrate the effectiveness of our method, in comparison with several representative cross-modal hashing methods.
机译：随着互联网和多媒体技术的发展，多媒体检索越来越需要诸如交叉模式哈希的大规模多模式表示技术。在跨模式哈希中，应认真考虑三个基本问题。首先是应从训练数据中缺乏标签信息的情况下学习有效的交叉模式关系。第二个是应为不同的方式分配适当的权重，以反映其重要性。最后是训练过程的可扩展性，通常被先前的方法所忽略。在本文中，我们综合考虑了这三点，提出了多图跨模态散列（MGCMH）。 MGCMH是一种无监督方法，它将多图学习和哈希函数学习集成到一个联合框架中，以学习所有模式的统一哈希空间。在MGCMH中，为不同的模态分配了适当的权重，分别用于生成多图和哈希码。结果，可以在哈希空间中保留更精确的交叉模式关系。然后，利用Nystrom近似方法来有效地构建图。最后，提出了一种交替学习算法，以联合优化模态权重，哈希码和函数。与几种代表性的跨模式散列方法相比，在两个真实世界的多模式数据集上进行的实验证明了我们方法的有效性。

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