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Vocabulary tree incremental indexing for scalable location recognition

  • Rongrong Ji*
  • , Xing Xie
  • , Hongxun Yao
  • , Yongjian Wu
  • , Wei Ying Ma
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Microsoft USA
  • Wuhan University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work aims at developing a scalable vision-based location recognition system where the backend database can be updated incrementally. Our proposed framework enables incremental indexing of vocabulary tree model, which efficiently includes new data into model refinement without re-generating entire model from overall dataset. An adaption trigger criterion is presented to lessen system computational cost, which is achieved by density-based relative entropy estimation between original dataset and newly coming data. Experiments on Seattle urban scene datasets with over 20K street-side images show the effectiveness of our work.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings
Pages869-872
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Hannover, Germany
Duration: 23 Jun 200826 Jun 2008

Publication series

Name2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings

Conference

Conference2008 IEEE International Conference on Multimedia and Expo, ICME 2008
Country/TerritoryGermany
CityHannover
Period23/06/0826/06/08

Keywords

  • Data similarity
  • Incremental indexing
  • Location recognition
  • Scene retrieval
  • Vocabulary tree

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