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Title

Learning features weights from user behavior in Content-Based Image Retrieval

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Published in S. J. Simoff and O. R. Zaiane. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Workshop on Multimedia Data Mining MDM/KDD2000). Boston (USA) - Aug 20-23 - . 2000
Abstract 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 a re analyzed over a period of four months. Within this period about 3500 accesses to the system were made w ith 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 q ueries contain enough information for the method described. Features frequently present in images marked together positively in the same que ry step get a higher weighting, whereas features present in one image marked positively and an other 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 importan t 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 re al users on a database containing 2500 images. The results of the system with learned weights are compared to the system withou t the learned feature weights.
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Research groups Viper group
Computer Vision and Multimedia Laboratory
Multimodal Interaction Group
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MULLER, Henning et al. Learning features weights from user behavior in Content-Based Image Retrieval. In: S. J. Simoff and O. R. Zaiane (Ed.). ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Workshop on Multimedia Data Mining MDM/KDD2000). Boston (USA). [s.l.] : [s.n.], 2000. https://archive-ouverte.unige.ch/unige:47850

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Deposited on : 2015-03-06

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