Concatenation hashing: A relative position preserving method for learning binary codes
作者：
Highlights：
• By employing the clustering technique and concatenating the substrings learnt by the hash functions in each cluster, the proposed method can model the complex relationship among the data and alleviate the effect brought from the boundary of the cluster.
• An alternating optimization is developed to simultaneously discover the cluster structures of the data and learn the hash functions to preserve the relative positions of the data to each cluster center.
• The experiments show that the proposed method is competitive to or better than other unsupervised hashing methods. Especially when learning the long codes in order to achieve the high search precision, the proposed method is obviously superior to the other methods.
摘要
•By employing the clustering technique and concatenating the substrings learnt by the hash functions in each cluster, the proposed method can model the complex relationship among the data and alleviate the effect brought from the boundary of the cluster.•An alternating optimization is developed to simultaneously discover the cluster structures of the data and learn the hash functions to preserve the relative positions of the data to each cluster center.•The experiments show that the proposed method is competitive to or better than other unsupervised hashing methods. Especially when learning the long codes in order to achieve the high search precision, the proposed method is obviously superior to the other methods.
论文关键词：Unsupervised hashing,Approximate nearest neighbor search,Clustering
论文评审过程：Received 10 July 2019, Revised 1 November 2019, Accepted 4 December 2019, Available online 6 December 2019, Version of Record 12 December 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.107151
