基于列表监督的Hash排序算法

杨安邦; 钱江波; 董一鸿; 陈华辉

doi:10.11959/j.issn.1000-0801.2019072

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基于列表监督的Hash排序算法

研究与开发 | 更新时间：2024-06-05

- 基于列表监督的Hash排序算法
- A ranking hashing algorithm based on listwise supervision
- 电信科学 2019年35卷第5期页码：78-85
- 作者机构：
- 作者简介：
  
  [ "杨安邦（1992- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为大数据、数据挖掘。" ]
  [ "钱江波（1974- ），男，博士，宁波大学信息科学与工程学院教授，主要研究方向为数据处理与挖掘、多维索引与查询优化、机器学习。" ]
  [ "董一鸿（1969- ），男，博士，宁波大学信息科学与工程学院教授，主要研究方向为大数据、数据挖掘和人工智能。" ]
  [ "陈华辉（1964- ），男，博士，宁波大学信息科学与工程学院教授，主要研究方向为数据处理与挖掘、云计算。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61472194);国家自然科学基金资助项目;The National Natural Science Foundation of China(61572266);浙江省自然科学基金资助项目;The Natural Science Foundation of Zhejiang Province of China(LY16F020003)
- DOI：10.11959/j.issn.1000-0801.2019072
  中图分类号： TP391
- 网络出版日期：2019-05，
  
  纸质出版日期：2019-05-20
- 稿件说明：
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杨安邦, 钱江波, 董一鸿, 等. 基于列表监督的Hash排序算法[J]. 电信科学, 2019,35(5):78-85.

Anbang YANG, Jiangbo QIAN, Yihong DONG, et al. A ranking hashing algorithm based on listwise supervision[J]. Telecommunications science, 2019, 35(5): 78-85.
杨安邦, 钱江波, 董一鸿, 等. 基于列表监督的Hash排序算法[J]. 电信科学, 2019,35(5):78-85. DOI： 10.11959/j.issn.1000-0801.2019072.

Anbang YANG, Jiangbo QIAN, Yihong DONG, et al. A ranking hashing algorithm based on listwise supervision[J]. Telecommunications science, 2019, 35(5): 78-85. DOI： 10.11959/j.issn.1000-0801.2019072.

摘要

Hash学习技术目前被广泛应用于大规模数据的相似性查找中，其通过将数据转化成二进制编码的形式，同时提高查找速度和降低存储代价。目前，大多数Hash排序算法通过比较数据在欧氏空间和海明空间的排序一致性来构造损失函数，然而，在海明空间的排序过程中，因为海明距离是离散的整数值，可能存在多个数据点共享相同的海明距离，这样就无法准确地排序。针对这一问题，将编码后的数据切分成几个长度相同的子空间，并为每个子空间设置不同的权重，比较时，再根据不同的子空间权重来计算海明距离。实验结果表明，与其他Hash学习算法相比，本文算法能够有效地对海明空间中的数据进行排序，并提高查询的准确性。

Abstract

Recently

learning to hash technology has been used for the similarity search of large-scale data.It can simultaneous increase the search speed and reduce the storage cost through transforming the data into binary codes.At present

most ranking hashing algorithms compare the consistency of data in the Euclidean space and the Hamming space to construct the loss function.However

because the Hamming distance is a discrete integer value

there may be many data points sharing the same Hamming distance result in the exact ranking cannot be performed.To address this challenging issue

the encoded data was divided into several subspaces with the same length.Each subspace was set with different weights.The Hamming distance was calculated according to different subspace weights.The experimental results show that this algorithm can effectively sort the data in the Hamming space and improve the accuracy of the query compared with other learning to hash algorithms.

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references

GUTTMAN A . R-trees:adynamic index structure for spatial searching [J ] . ACM Sigmod Record , 1984 , 14 ( 2 ): 47 - 57 .

SONGD , LIU W , JI R . Top rank supervised binary coding for visual search [C ] // 2015 IEEE International Conference on Computer Vision,Dec 13-16,2015,Sontiago,Chile . Piscataway:IEEE Press , 2015 .

PARK Y , CAFARELLA M , MOZAFARI B . Neighbor-sensitive Hashing [M ] . New York : ACM PressPress , 2016 .

WANG J , ZHANG T , SONG J , et al . A survey on learning to hash [J ] . IEEE Transaction on Pattern Analysis and Machine Intelligence , 2016 ( 99 ):1.

DATAR M , IMMORLICA N , INDYK P , et al . Locality-sensitive hashing scheme based on p-stable distributions [C ] // Twentieth Symposium on Computational Gemetry,Jun 8-11,2004,New York,USA . New York:ACM Press , 2004 :253.

WEISS Y , TORRALBA A , FERGUS R . Spectral Hashing [C ] // Neural Information Processing System,Dec 8-11,2008,British Columbia,Canada . New York:ACM Press , 2008 .

ZHANGD , WANG J , CAI D , et al . Self-taught hashing for fast similarity search [C ] // 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval,July 19-23,2010,IGO,Geneva Switzerland . New York:ACM Press , 2010 .

GONG Y , LAZEBINK S , GORDO A , et al . Iterative quantization:a procrustean approach to learning binary codes for large-scale image retrieval [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2013 , 135 ( 12 ): 2916 - 2919 .

LIN R S , ROSS D A , YAGNIK J . Spec hashing:similarity preserving algorithm for entropy-based coding [C ] // Computer Vision and Pattern Recognition,Jun 13-18,2010,San Francisco,CA,USA.[S.l.:sn] . 2010 .

NOROUZI M E , FLEETD J . Minimal loss hashing for compact binary codes [C ] // The 28th International Conference on Machine Learning,June 28-July 2,2011,Washington,USA . Piscataway:IEEE Press , 2011 .

CHANG S F , JIANG Y G , JI R , et al . Supervised hashing with kernels [C ] // Computer Vision and Pattern Recognition,June 16-21,2012,Providence,RI,USA . Piscataway:IEEE Press , 2012 .

SHEN F , SHEN C , LIU W . Supervised discrete hashing [C ] // Computer Vision and Pattern Recognition,June 8-10,2015,Boston,USA . Piscataway:IEEE Press , 2015 .

STRECHA C , BRONSTEIN A , BRONSTEIN M , et al . LDAHash:improved matching with smaller descriptors [J ] . IEEE Transactions actions on Pattern Analysis and Machine Intelligence , 2011 , 34 ( 1 ): 66 - 78 .

WANG J , KUMA R S , CHANG S F . Semi-supervised hashing for large-scale search [J ] . IEEE Transactions on Pattern Analysis Machine Intelligence , 2012 , 34 ( 12 ): 2393 - 2406 .

LIN G , SHEN C , WU J . Optimizing ranking measures for compact binary code learning [Z ] . 2014 .

SONG D , LIU W , MEYER D , et al . Rank preserving hashing for rapid image search [C ] // 2015 Data Compression Conference,April 7-9,2015,Snowbird,UT,USA . Piscataway:IEEE Press , 2015 .

JIANG Y , WANG J , XUE X . Query-adaptive image search with hash codes [J ] . IEEE Transactions on Multimedia , 2013 , 15 ( 2 ): 442 - 453 .

SHUM H Y , . QsRank:query-sensitive hash code ranking for efficient ε-nighbor search [C ] // IEEE Conference on and Pattern Recognition,June 16-21,2012,Providence,RI,USA . Piscataway:IEEE Press , 2012 .

JI T , LIU X , DENG C , et al . Query-adaptive hash code ranking for fast nearest neighbor search [C ] // ACM International Conference on Multimedia,Dec 3-7,2014,Orlando,FL,USA . New York:ACM Press , 2014 .

LI X , LIN G , SHEN C , et al . Learning hash functions using column generation [J ] . Computer Science , 2013 : 142 - 150 .

NOROUZI M , FLEET D J , SALAKHUTDINOV R . Hamming distance metric learning [C ] // International Conference on Neural Information Processing System,Dec 3-6,2012,Lake Tahoe,Nevada . New York:ACM Press , 2012 : 1061 - 1069 .

WANG J , LIU W , SUN A X , et al . Learning hash codes with listwise supervision [C ] // The 2013 IEEE International Conference on Computer Vision,Dec 1-8,2013,Sydney,Australia . Piscataway:IEEE Press , 2013 : 3032 - 3039 .

WANG Q , ZHANG Z , SI L . Ranking preserving hashing for fast similiarity search [C ] // IJCAI,July 25-31,2015,Buenos Aires,Argentina.[S.l.:s . n] , 2015 .

YAO T , LONG F , MEI T , et al . Deep semantic preserving and ranking-based hashing for image retrieval [C ] // IJCAI,July 9-15,2016,New York,USA.[S.l.:sn] . 2016 .

LIU L , SHAO L , SHEN F , et al . Discretely coding semantic rank orders for supervised image hashing [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition,July 21-26,2017,Honolulu,HI,USA . Piscataway:IEEE Press , 2017 .

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