Multimedia Tools and Applications

, Volume 78, Issue 21, pp 30537–30560 | Cite as

Hierarchical one permutation hashing: efficient multimedia near duplicate detection

  • Chengyuan Zhang
  • Yunwu Lin
  • Lei Zhu
  • XinPan YuanEmail author
  • Jun LongEmail author
  • Fang Huang


With advances in multimedia technologies and the proliferation of smart phone, digital cameras, storage devices, there are a rapidly growing massive amount of multimedia data collected in many applications such as multimedia retrieval and management system, in which the data element is composed of text, image, video and audio. Consequently, the study of multimedia near duplicate detection has attracted significant concern from research organizations and commercial communities. Traditional solution minwish hashing (MinWise) faces two challenges: expensive preprocessing time and lower comparison speed. Thus, this work first introduce a hashing method called one permutation hashing (OPH) to shun the costly preprocessing time. Based on OPH, a more efficient strategy group based one permutation hashing (GOPH) is developed to deal with the high comparison time. Based on the fact that the similarity of most multimedia data is not very high, this work design an new hashing method namely hierarchical one permutation hashing (HOPH) to further improve the performance. Comprehensive experiments on real multimedia datasets clearly show that with similar accuracy HOPH is five to seven times faster than MinWise.


Hierarchical one permutation hashing Multimedia data Near duplicate detection 



This work was supported in part by the National Natural Science Foundation of China (61379110, 61472450, 61702560), the Key Research Program of Hunan Province(2016JC2018), project (2016JC2011, 2018JJ3691) of Science and Technology Plan of Hunan Province, and Fundamental Research Funds for Central Universities of Central South University (2018zzts588).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018
corrected publication July/2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaPeople’s Republic of China
  2. 2.Big Data and Knowledge Engineering InstituteCentral South UniversityChangshaPeople’s Republic of China
  3. 3.School of ComputerHunan University of TechnologyZhuzhouPeople’s Republic of China

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