Proceedings Chapter
Reference
Learning features weights from user behavior in Content-Based Image Retrieval
MULLER, Henning, et al.
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 [...]
MULLER, Henning, et al . Learning features weights from user behavior in Content-Based Image Retrieval. 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) . 2000.
Available at:
http://archive-ouverte.unige.ch/unige:47850
Disclaimer: layout of this document may differ from the published version.
1 / 1
Learning Feature Weights from User Behavior in Content-Based Image Retrieval
Henning M ¨uller, Wolfgang M ¨uller, St ´ephane Marchand-Maillet, Thierry Pun
Computer Vision Group, University of Geneva 24 Rue du G ´en ´eral Dufour,
CH-1211 Gen `eve 4, Switzerland
henning.mueller@cui.unige.ch
David McG Squire
Computer Science and Software Engineering Monash University
Melbourne, Australia
ABSTRACT
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Precision
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Precision
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0 0.2 0.4 0.6 0.8 1
Precision
;
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With learnt factor 1 of TSR database With learnt factor 2 of TSR database With learnt factor 1 of all databases With learnt factor 2 of all databases
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0 0.2 0.4 0.6 0.8 1
Precision
;
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With learnt factor 1 of TSR database With learnt factor 2 of TSR database With learnt factor 1 of all databases With learnt factor 2 of all databases
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5. CONCLUSIONS AND FURTHER WORK
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6. REFERENCES
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