Technical report
Open access

Strategies for positive and negative relevance feedback in image retrieval

  • Technical report VISION; 00.01
Publication date2000

Relevance feedback has been shown to be a very effective tool for enhancing retrieval results in text retrieval. In content-based image retrieval it is more and more frequently used and very good results have been obtained. However, too much negative feedback may destroy a query as good features get negative weightings. This paper compares a variety of strategies for positive and negative feedback. The performance evaluation of feedback algorithms is a hard problem. To solve this, we obtain judgments from several users and employ an automated feedback scheme. We can then evaluate different techniques using the same judgments. Using automated feedback, the ability of a system to adapt to the user's needs can be measured very effectively. Our study highlights the utility of negative feedback, especially over several feedback steps.

Citation (ISO format)
MULLER, Henning et al. Strategies for positive and negative relevance feedback in image retrieval. 2000
Main files (1)
  • PID : unige:48031

Technical informations

Creation03/09/2015 11:34:28 AM
First validation03/09/2015 11:34:28 AM
Update time03/14/2023 11:00:31 PM
Status update03/14/2023 11:00:31 PM
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