Congruency-based reranking

Itai Ben-Shalom, Noga Levy, Lior Wolf, Nachum Dershowitz, Adiel Ben-Shalom, Roni Shweka, Yaacov Choueka, Tamir Hazan, Yaniv Bar

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

Abstract

We present a tool for re-ranking the results of a specific query by considering the (n+1) × (n+1) matrix of pairwise similarities among the elements of the set of n retrieved results and the query itself. The re-ranking thus makes use of the similarities between the various results and does not employ additional sources of information. The tool is based on graphical Bayesian models, which reinforce retrieved items strongly linked to other retrievals, and on repeated clustering to measure the stability of the obtained associations. The utility of the tool is demonstrated within the context of visual search of documents from the Cairo Genizah and for retrieval of paintings by the same artist and in the same style.

Original languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages2107-2114
Number of pages8
ISBN (Electronic)9781479951178
DOIs
StatePublished - 24 Sep 2014
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: 23 Jun 201428 Jun 2014

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Conference

Conference27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
Country/TerritoryUnited States
CityColumbus
Period23/06/1428/06/14

Keywords

  • clustering
  • genizah
  • graphical models
  • image retrieval
  • query retrieval
  • reranking

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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