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Scientific article
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Learning from user behavior in image retrieval : application of market basket analysis

Contributors

Muller, Henning; Pun, Thierry; Squire, David

Published in

International journal of computer vision, vol. 56, no. 1-2, p. 65-77

Publication date

2004

Abstract

This article describes an approach to learn feature weights for content-based image retrieval (CBIR) from user interaction log files. These usage log files are analyzed for images marked together by a user in the same query step. The problem is somewhat similar to one of the traditional data mining problems, the market basket analysis problem, where items bought together in a supermarket are analyzed. This paper outlines similarities and differences between the two fields and explains how to use the interaction data for deriving a better feature weighting.Experiments with existing log files are done and a significant improvement in performance is reached with a feature weighting calculated from the information contained in the log files. Even with several steps of relevance feedback the results remain much better than without the learning, which means that not only information from feedback is taken into account earlier, but a better quality of retrieval is reached in all steps.

Keywords

  • content-based image retrieval
  • market basket analysis
  • learning from user interaction

UNIGE affiliation entities

UNIGE research groups

Citation (ISO format)

MULLER, Henning, PUN, Thierry, SQUIRE, David. Learning from user behavior in image retrieval : application of market basket analysis. In: International journal of computer vision, 2004, vol. 56, n° 1-2, p. 65–77. doi: 10.1023/B:VISI.0000004832.02269.45

Main files (2)

Article (Published version)
Article (Accepted version)

Identifiers

Additional URL for this publication

http://link.springer.com/article/10.1023/B%3AVISI.0000004832.02269.45

Journal ISSN

0920-5691
630views
1029downloads

Technical informations

Creation

03/03/2015 16:36:11

First validation

03/03/2015 16:36:11

Update

14/03/2023 22:56:46

Status update

14/03/2023 22:56:46

Last indexation

30/10/2024 23:11:03
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