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Building and tracking hierarchical geographical & temporal partitions for image collection management on mobile devices
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Source International Multimedia Conference archive
Proceedings of the 13th annual ACM international conference on Multimedia table of contents
Hilton, Singapore
SESSION: Content 2: image clustering table of contents
Pages: 141 - 150  
Year of Publication: 2005
ISBN:1-59593-044-2
Authors
A. Pigeau  INRIA Ecole polytechnique de l'université de Nantes, cedex - France
M. Gelgon  INRIA Ecole polytechnique de l'université de Nantes, cedex - France
Sponsors
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 5,   Downloads (12 Months): 46,   Citation Count: 7
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ABSTRACT

Usage of mobile devices (phones, digital cameras) raises the need for organizing large personal image collections. In accordance with studies on user needs, we propose a statistical criterion and an associated optimization technique, relying on geo-temporal image metadata, for building and tracking a hierarchical structure on the image collection. In a mixture model framework, particularities of the application and typical data sets are taken into account in the design of the scheme (incrementality, ability to cope with non-Gaussian data, with both small and large samples). Results are reported on real data sets.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Aris, A., Gemmel, J., and Lueder, R. Exploiting location and time for photo search and storytelling in MyLifeBits. Tech. Rep. MSR-TR-2004-102, Microsoft research, Sept. 2004.
 
2
 
3
 
4
Bishop, C., and Svensen, M. Robust Bayesian mixture modelling. In Proceedings Twelfth European Symposium on Artificial Neural Networks (Bruges, Belgium, Apr. 2004), pp. 69--74.
5
6
 
7
 
8
Gargi, U., Deng, Y., and Tretter, D. R. Managing and searching personal photo collections. Tech. Rep. HPL-2002-67, HP Laboratories, Palo Alto, Mar. 2002.
 
9
Gelgon, M., and Tilhou., K. Structuring the personal multimedia collection of a mobile device user based on geolocation. In IEEE Int. conf. on Multimedia and Expo (ICME'2002) (Lausanne, Switzerland, Aug. 2002), pp. 248--252.
10
11
 
12
Loui, A., and Savakis, A. E. Automatic image event segmentation and quality screening for albuming applications. In IEEE Proceedings Int. Conf. on Multimedia and Expo (ICME'2000) (New York, USA, Aug. 2000), pp. 1125--1128.
 
13
Luo, J., Savakis, A., and Singhal, A. A Bayesian network-based framework for semantic image understanding. Pattern Recognition 38, 6 (June 2005), 919--934.
 
14
Myka, A. Nokia lifeblog - towards a truly personal multimedia information system. In Proc. of Workshop des GI-Arbeitkreises "Mobile Datenbanken and Informationsysteme(Karlsruhe, Germany, Feb. 2005).
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Pigeau, A., and Gelgon, M. Organizing a personal image collection with statistical model-based ICL clustering on spatio-temporal camera phone meta-data Journal of Visual Communication and Image Representation 15 3(2004), pp.-425--445.
 
17
 
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Platt, J. C., and M. Czerwinski, B. A. F. PhotoTOC: Automatic clustering for browsing personal photographs. Tech. Rep. MSR-TR-2002-17, Microsoft Research, Feb. 2002.
19
20
 
21
Ueda, N., Nakano, R., Gharhamani, Z., and Hinton, G. SMEM algorithm for mixture models. Neural computation 12, 9 (2000), 2109--2128.
 
22
Vermaak, J. Perez, P., and Gangnet, M. Rapid summarization and browsing of video sequences. In Proc. of British Machine Vision Conference (BVMC'2002) (Cardiff, U.K., Sept. 2002), pp. 145--151.
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CITED BY  7