Hetero-Manifold Regularisation for Cross-Modal Hashing
Hetero-Manifold Regularisation for Cross-Modal Hashing
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DOI：
10.1109/tpami.2016.2645565
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发表时间：
2018-05
影响因子：
23.6
通讯作者：
Feng Zheng;Yi Tang;Ling Shao
中科院分区：
文献类型：
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作者：
Feng Zheng;Yi Tang;Ling Shao
近年来，由于多模态数据的集成复杂性和异构性，跨模态搜索受到了广泛的关注，但仍然是一个非常具有挑战性的任务。为了解决这两个挑战，在本文中，我们提出了一种新的方法，称为异流形正则化（HMR），以监督哈希函数的学习，以实现高效的跨模态搜索。一个异质流形集成多个子流形定义的同质数据的帮助下，跨模态监督信息。利用异质流形的优势，每对异质数据之间的相似性可以自然地通过该异质流形上的三阶随机游动来度量。此外，引入了一个新的定义在异质流形上的累积距离不等式，以避免哈希码的离散性所带来的计算困难。通过使用不等式，跨模态哈希转换成一个问题的异质流形正则化的支持向量机学习。因此，跨模态搜索的性能可以通过将异质流形的集成信息和支持向量机的强大泛化能力无缝结合来显着提高。综合实验表明，所提出的HMR实现了优越的结果，在几个具有挑战性的跨模态任务的国家的最先进的方法。
Recently, cross-modal search has attracted considerable attention but remains a very challenging task because of the integration complexity and heterogeneity of the multi-modal data. To address both challenges, in this paper, we propose a novel method termed hetero-manifold regularisation (HMR) to supervise the learning of hash functions for efficient cross-modal search. A hetero-manifold integrates multiple sub-manifolds defined by homogeneous data with the help of cross-modal supervision information. Taking advantages of the hetero-manifold, the similarity between each pair of heterogeneous data could be naturally measured by three order random walks on this hetero-manifold. Furthermore, a novel cumulative distance inequality defined on the hetero-manifold is introduced to avoid the computational difficulty induced by the discreteness of hash codes. By using the inequality, cross-modal hashing is transformed into a problem of hetero-manifold regularised support vector learning. Therefore, the performance of cross-modal search can be significantly improved by seamlessly combining the integrated information of the hetero-manifold and the strong generalisation of the support vector machine. Comprehensive experiments show that the proposed HMR achieve advantageous results over the state-of-the-art methods in several challenging cross-modal tasks.