An extendible hash for multi-precision similarity querying of image databases

Shu Lin, M. Tamer Özsu, Vincent Oria, Raymond Ng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Scopus citations

Abstract

We propose multi-precision similarity matching where the image is divided into a number of subblocks, each with its associated color histogram. We present experimental results showing that the spatial distribution information recorded by multiprecision color histograms helps to make similarity matching more precise. We also show that sub-image queries are much better supported with multi-precision color histograms. To minimize the overhead, we employ a filtering scheme based on the 3-dimensional average color vectors. We provide a formal result proving that filtering with multi-precision color histograms is complete. Finally, we develop a novel extendible hashing structure for indexing the average color vectors. We give experimental results showing that the proposed structure significantly outperforms the SR-tree.

Original languageEnglish (US)
Title of host publicationVLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases
EditorsPeter M. G. Apers, Paolo Atzeni, Richard T. Snodgrass, Stefano Ceri, Kotagiri Ramamohanarao, Stefano Paraboschi
PublisherMorgan Kaufmann
Pages221-230
Number of pages10
ISBN (Electronic)1558608044, 9781558608047
StatePublished - Jan 1 2001
Event27th International Conference on Very Large Data Bases, VLDB 2001 - Roma, Italy
Duration: Sep 11 2001Sep 14 2001

Publication series

NameVLDB 2001 - Proceedings of 27th International Conference on Very Large Data Bases

Other

Other27th International Conference on Very Large Data Bases, VLDB 2001
Country/TerritoryItaly
CityRoma
Period9/11/019/14/01

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Computer Science Applications
  • Hardware and Architecture
  • Software
  • Computer Networks and Communications
  • Information Systems

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