Geo-temporal structuring of a personal image database with two-level variational-bayes mixture estimation

Authors:
Pierrick Bruneau;Antoine Pigeau;Marc Gelgon;Fabien Picarougne
Affiliations:
Nantes university, LINA (UMR CNRS 6241), Polytech’Nantes, Nantes cedex 3, France;Nantes university, LINA (UMR CNRS 6241), Polytech’Nantes, Nantes cedex 3, France;Nantes university, LINA (UMR CNRS 6241), Polytech’Nantes, Nantes cedex 3, France;Nantes university, LINA (UMR CNRS 6241), Polytech’Nantes, Nantes cedex 3, France
Venue:
AMR'08 Proceedings of the 6th international conference on Adaptive Multimedia Retrieval: identifying, Summarizing, and Recommending Image and Music
Year:
2008

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Abstract

This paper addresses unsupervised hierarchical classification of personal documents tagged with time and geolocation stamps. The target application is browsing among these documents. A first partition of the data is built, based on geo-temporal measurement. The events found are then grouped according to geolocation. This is carried out through fitting a two-level hierarchy of mixture models to the data. Both mixtures are estimated in a Bayesian setting, with a variational procedure: the classical VBEM algorithm is applied for the finer level, while a new variational-Bayes-EM algorithm is introduced to search for suitable groups of mixture components from the finer level. Experimental results are reported on artificial and real data.