%Aigaion2 BibTeX export from HES SO Valais Publications
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@INPROCEEDINGS{MMS2000a,
     author = {M{\"{u}}ller, Henning and M{\"{u}}ller, Wolfgang and Squire, David McG. and Marchand-Maillet, St{\'{e}}phane and Pun, Thierry},
     editor = {Sanfeliu, A. and Villanueva, J. J. and Vanrell, M. and Alc{\'{e}}zar, R. and Eklundh, J. -O. and Aloimonos, Y.},
   keywords = {evaluation, image retrieval, negative relevance feedback, relevance feedback},
      month = september,
      title = {Strategies for positive and negative relevance feedback in image retrieval},
  booktitle = {Proceedings of the 15th International Conference on Pattern Recognition (ICPR 2000)},
       year = {2000},
      pages = {1043-1046},
  publisher = {IEEE},
    address = {Barcelona, Spain},
        url = {http://cui.unige.ch/~vision/Publications/postscript/2000/VGTR00.01_HMuellerWMuellerSquireMarchandPun.ps},
   abstract = {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.},
vgproject={viper},
url1={http://cui.unige.ch/~vision/Publications/postscript/2000/VGTR00.01\_HMuellerWMuellerSquireMarchandPun.pdf},
}