Long-term relevance feedback and feature selection for adaptive content based image suggestion

Authors:
Sabri Boutemedjet;Djemel Ziou
Affiliations:
Département de Génie de la Production Automatisée, ícole de Technologie Supérieure, Montreal, Canada and Département d'Informatique, Université de Sherbrooke, Sh ...;Département de Génie de la Production Automatisée, ícole de Technologie Supérieure, Montreal, Canada and Département d'Informatique, Université de Sherbrooke, Sh ...
Venue:
Pattern Recognition
Year:
2010

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Abstract

Content-based image suggestion (CBIS) addresses the satisfaction of users long-term needs for ''relevant'' and ''novel'' images. In this paper, we present VCC-FMM, a flexible mixture model that clusters both images and users into separate groups. Then, we propose long-term relevance feedback to maintain accurate modeling of growing image collections and changing user long-term needs over time. Experiments on a real data set show merits of our approach in terms of image suggestion accuracy and efficiency.