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.
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Bruneau, P., Pigeau, A., Gelgon, M., Picarougne, F. (2010). Geo-temporal Structuring of a Personal Image Database with Two-Level Variational-Bayes Mixture Estimation. In: Detyniecki, M., Leiner, U., Nürnberger, A. (eds) Adaptive Multimedia Retrieval. Identifying, Summarizing, and Recommending Image and Music. AMR 2008. Lecture Notes in Computer Science, vol 5811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14758-6_11
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DOI: https://doi.org/10.1007/978-3-642-14758-6_11
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