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  • Henning Müller, Wolfgang Müller, David McG. Squire, Stéphane Marchand-Maillet and Thierry Pun, Learning Feature Weights from User Behavior in Content-Based Image Retrieval, In MDM/KDD2000 Workshop on Multimedia Data Mining in conjunction with the Sixth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Boston, USA, August 20 2000.

    This article describes an algorithm for obtaining knowledge about the importance of features from analyzing user log files of a content-based image retrieval system (CBIRS). The user log files from the usage of the Viper web demonstration system are analyzed over a period of four months. Within this period about 3500 accesses to the system were made with almost 800 multiple image queries. All the actions of the users were logged in a file.

    The analysis only includes multiple image queries of the system with positive and/or negative input images, because only multiple image queries contain enough information for the method described. Features frequently present in images marked together positively in the same query step get a higher weighting, whereas features present in one image marked positively and another image marked negatively in the same step get a lower weighting. The Viper system offers a very large number of simple features. This allows the creation of flexible feature weightings with high values for important and low values for less important features. These weightings for features can of course differ between collections and as well between users. The results are evaluated with an experiment using the relevance judgments of real users on a database containing 2500 images. The results of the system with learned weights are compared to the system without the learned feature weights.