Co-Regularized Hashing for Multimodal Data
Co-Regularized Hashing for Multimodal Data
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发表时间：
2012-12
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通讯作者：
Yi Zhen;D. Yeung
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作者：
Yi Zhen;D. Yeung
基于散列的方法为大规模相似性搜索提供了一种非常有前途的方法。为了获得紧凑的哈希码，最近的趋势是从数据中自动学习哈希函数。在本文中，我们研究了多模态数据背景下的哈希函数学习。我们提出了一种新的多模态散列函数学习方法，称为共正则化散列（CRH），基于提升的共正则化框架。散列码的每个比特的散列函数通过求解DC（凸函数差）程序来学习，而多个比特的学习通过提升过程进行，使得由散列函数引入的偏差可以顺序地最小化。我们在两个公开的数据集上将CRH与两种最先进的多模态散列函数学习方法进行了经验比较。
Hashing-based methods provide a very promising approach to large-scale similarity search. To obtain compact hash codes, a recent trend seeks to learn the hash functions from data automatically. In this paper, we study hash function learning in the context of multimodal data. We propose a novel multimodal hash function learning method, called Co-Regularized Hashing (CRH), based on a boosted co-regularization framework. The hash functions for each bit of the hash codes are learned by solving DC (difference of convex functions) programs, while the learning for multiple bits proceeds via a boosting procedure so that the bias introduced by the hash functions can be sequentially minimized. We empirically compare CRH with two state-of-the-art multimodal hash function learning methods on two publicly available data sets.