Order preserving hashing for approximate nearest neighbor search
Order preserving hashing for approximate nearest neighbor search
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DOI：
10.1145/2502081.2502100
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Jianfeng Wang;Jingdong Wang;Nenghai Yu;Shipeng Li
中科院分区：
文献类型：
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作者：
Jianfeng Wang;Jingdong Wang;Nenghai Yu;Shipeng Li
在本文中，我们提出了一种新的方法来学习用于近似最近邻（NN）搜索的保持相似性的哈希函数。其关键思想是通过最大化从原始空间计算的相似性顺序与汉明空间中的相似性顺序之间的对齐来学习哈希函数。将NN点映射到不同的哈希码的问题被视为一个分类问题，其中根据到查询的汉明距离将点分类为若干组。散列函数是从在训练点上汇集的分类器中优化的。实验结果表明，我们的方法优于现有的国家的最先进的哈希技术。
In this paper, we propose a novel method to learn similarity-preserving hash functions for approximate nearest neighbor (NN) search. The key idea is to learn hash functions by maximizing the alignment between the similarity orders computed from the original space and the ones in the hamming space. The problem of mapping the NN points into different hash codes is taken as a classification problem in which the points are categorized into several groups according to the hamming distances to the query. The hash functions are optimized from the classifiers pooled over the training points. Experimental results demonstrate the superiority of our approach over existing state-of-the-art hashing techniques.