Geo-temporal Structuring of a Personal Image Database with Two-Level Variational-Bayes Mixture Estimation

  • Pierrick Bruneau
  • Antoine Pigeau
  • Marc Gelgon
  • Fabien Picarougne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5811)


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.


Mixture Model Digital Library Image Author Mixture Parameter Photo Collection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Rodden, K.: How do people manage their digital photographs? In: ACM Conference on Human Factors in Computing Systems, Fort Lauderdale, pp. 409–416 (2003)Google Scholar
  2. 2.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  3. 3.
    Blekas, K., Lagaris, I.E.: Split-merge incremental learning (smile) of mixture models. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007, Part II. LNCS, vol. 4669, pp. 291–300. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Celeux, G., Govaert, G.: A classification em algorithm for clustering and two stochastic versions. Computational Statistics and Data Analysis (1992)Google Scholar
  5. 5.
    Celeux, G., Govaert, G.: Gaussian parcimonious clustering models. Pattern Recognition 28, 781–793 (1995)CrossRefGoogle Scholar
  6. 6.
    Fraley, C., Raftery, A.E.: Mclust: Software for model-based clustering, density estimation and discriminant analysis. Technical report 415, Department of Statistics - University of Washington (2002)Google Scholar
  7. 7.
    Fraley, C., Raftery, A.E.: Model-based clustering, discriminant analysis and density estimation. Journal of the American Statistical Association (2002)Google Scholar
  8. 8.
    Pigeau, A., Gelgon, M.: Building and tracking hierarchical geographical & temporal partitions for image collection management on mobile devices. In: Proceedings of International Conference of ACM Multimedia, Singapore, pp. 141–150 (2005)Google Scholar
  9. 9.
    Attias, H.: A variational bayesian framework for graphical models. In: Advances in Neural Information Processing Systems (2000)Google Scholar
  10. 10.
    Vasconcelos, N.: Image indexing with mixture hierarchies. In: Proceedings of IEEE Conference in Computer Vision and Pattern Recognition (2001)Google Scholar
  11. 11.
    Goldberger, J., Roweis, S.: Hierarchical clustering of a mixture model. In: NIPS (2004)Google Scholar
  12. 12.
    Graham, A., Garcia-Molina, H., Paepcke, A., Winograd, T.: Time as essence for photo browsing through personal digital libraries. In: Proceedings of the ACM Joint Conference on Digital Libraries JCDL, pp. 326–335 (2002)Google Scholar
  13. 13.
    Platt, J.C., Czerwinski, M., Field, B.A.: PhotoTOC: Automatic clustering for browsing personal photographs. Technical Report MSR-TR-2002-17, Microsoft Research (2002)Google Scholar
  14. 14.
    Toyama, K., Logan, R., Roseway, A., Anandan, P.: Geographic location tags on digital images. In: Proceedings of the eleventh ACM international conference on Multimedia, Berkeley, CA, USA, pp. 156–166 (2003)Google Scholar
  15. 15.
    Jaffe, A., Naaman, M., Tassa, T., Davis, M.: Generating summaries and visualization for large collections of geo-referenced photographs. In: Proceedings of the 8th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 853–854 (2006)Google Scholar
  16. 16.
    Naaman, M., Song, Y.J., Paepcke, A., Garcia-Molina, H.: Automatic organization for digital photographs with geographic coordinates. In: Proceedings of the ACM/IEEE Conference on Digital libraries (JCDL 2004), pp. 53–62 (2004)Google Scholar
  17. 17.
    Cooper, M., Foote, J., Girgensohn, A., Wilcox, L.: Temporal event clustering for digital photo collections. In: Proceedings of the ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), vol. 1, pp. 269–288 (2005)Google Scholar
  18. 18.
    Gargi, U., Deng, Y., Tretter, D.R.: Managing and searching personal photo collections. Technical Report HPL-2002-67, HP Laboratories, Palo Alto (2002)Google Scholar
  19. 19.
    Kennedy, L., Naaman, M.: Generating diverse and representative image search results for landmarks. In: Proceedings of The Seventeenth International World Wide Web Conference, WWW 2008 (2008)Google Scholar
  20. 20.
    Nair, R., Reid, N., Davis, M.: Photo loi: Browsing multi-user photo collections. In: Proceedings of International Conference of ACM Multimedia, pp. 222–223 (2005)Google Scholar
  21. 21.
    O’Hare, N., Gurrin, C., Jones, G., Smeaton, A.F.: Combination of content analysis and context features for digital photograph retrieval. In: Proceedings of of the 2nd IEE European Workshop on the Integration of Knowledge, Semantic and Digital Media Technologies, pp. 323–328 (2005)Google Scholar
  22. 22.
    Akaike, H.: A new look at the statistical model identification. IEEE Trans. on Automatic Control AC-19(6) (1974)Google Scholar
  23. 23.
    Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6, 461–464 (1978)zbMATHCrossRefMathSciNetGoogle Scholar
  24. 24.
    Vasconcelos, N., Lippman, A.: Learning mixture hierarchies. In: Neural Information Processing Systems (1998)Google Scholar
  25. 25.
    Blahut, R.E.: Principles and Practice of Information Theory. Addison-Wesley, Reading (1987)zbMATHGoogle Scholar
  26. 26.
    Evett, I.W., Spiehler, E.J.: Rule induction in forensic science. Ellis Horwood in Expert Systems, Knowledge Based Systems, 152–160 (1989)Google Scholar
  27. 27.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the em algorithm. J. Royal Stat. Soc. B(39), 1–38 (1977)MathSciNetGoogle Scholar
  28. 28.
    Azzag, H.: Classification hiérarchique par des fourmis artificielles: application à la fouille de données et de textes pour le Web. PhD thesis, Ecole Doctorale Santé Sciences et Technologies, Université François Rabelais Tours (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Pierrick Bruneau
    • 1
    • 2
  • Antoine Pigeau
    • 1
  • Marc Gelgon
    • 1
    • 2
  • Fabien Picarougne
    • 1
  1. 1.Nantes university, LINA (UMR CNRS 6241), Polytech’NantesNantes cedex 3France
  2. 2.INRIA/IRISA Atlas project-team 

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