A Graphical Model for Content Based Image Suggestion and Feature Selection

Authors:
Sabri Boutemedjet;Djemel Ziou;Nizar Bouguila
Affiliations:
Département d'informatique, Université de Sherbrooke, QC, J1K 2R1, Canada;Département d'informatique, Université de Sherbrooke, QC, J1K 2R1, Canada;Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, H3G 1T7, Canada
Venue:
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2007

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Abstract

Content based image retrieval systems provide techniques for representing, indexing and searching images. They address only the user's short term needs expressed as queries. From the importance of the visual information in many applications such as advertisements and security, we motivate in this paper, the Content Based Image Suggestion. It targets the user's long term needs as a recommendation of products based on the user preferences in different situations, and on the visual content of images. We propose a generative model in which the visual features and users are clustered into separate classes. We identify the number of both user and image classes with the simultaneous selection of relevant visual features. The goal is to ensure an accurate prediction of ratings for multidimensional images. This model is learned using the minimum message length approach. Experiments with an image collection showed the merits of our approach.