Learning feature weights from user behavior in content-based image retrieval
Henning Müller, Wolfgang Müller, Stéphane Marchand‐Maillet, Thierry Pun, David Squire · 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 exible feature weighting...