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

Abstract : This paper addresses unsupervised hierarchical classication of personal documents tagged with time and geolocation stamps. The target application is browsing among these documents. A rst 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 tting a two-level hierarchy of mixture models to the data. Both mixtures are estimated in a Bayesian setting, with a variational proce- dure: the classical VBEM algorithm is applied for the ner level, while a new variational-Bayes-EM algorithm is introduced to search for suitable groups of mixture components from the ner level. Experimental results are reported on articial and real data.
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https://hal.inria.fr/inria-00396790
Contributor : Marc Gelgon <>
Submitted on : Thursday, June 18, 2009 - 7:00:54 PM
Last modification on : Wednesday, April 11, 2018 - 1:56:49 AM
Long-term archiving on: : Monday, October 15, 2012 - 2:35:52 PM

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  • HAL Id : inria-00396790, version 1

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Pierrick Bruneau, Antoine Pigeau, Marc Gelgon, Fabien Picarougne. Geo-temporal structuring of a personal image database with two-level variational-Bayes mixture estimation. Adaptive Multimedia Retrieval workshop (AMR'08), HHI Berlin, Jun 2008, Berlin, Germany. pp.127-139. ⟨inria-00396790⟩

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