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Vocabulary tree incremental indexing for scalable location recognition

  • Rongrong Ji
  • , Xing Xie
  • , Hongxun Yao
  • , Yongjian Wu
  • , Wei-Ying Ma

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-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. © 2008 IEEE.
Original languageEnglish
Title of host publication2008 IEEE International Conference on Multimedia and Expo, ICME 2008 - Proceedings
Pages869-872
DOIs
Publication statusPublished - 2008
Externally publishedYes
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
PlaceGermany
CityHannover
Period23/06/0826/06/08

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

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

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