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Food log by analyzing food images
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International Multimedia Conference archive
Proceeding of the 16th ACM international conference on Multimedia table of contents
Vancouver, British Columbia, Canada
DEMONSTRATION SESSION: Demo session 1 table of contents
Pages 999-1000  
Year of Publication: 2008
ISBN:978-1-60558-303-7
Authors
Keigo Kitamura  The University of Tokyo, Tokyo, Japan
Toshihiko Yamasaki  The University of Tokyo, Tokyo, Japan
Kiyoharu Aizawa  The University of Tokyo, Tokyo, Japan
Sponsors
ACM: Association for Computing Machinery
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
Publisher
ACM  New York, NY, USA
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ABSTRACT

In this paper, a food-logging system that can distinguish food images from other images, analyze the food balance, and visualize the log is presented. The image processing is based on feature vectors consisting of color histograms, DCT coefficients, detected image patterns and so forth. Support Vector Machine (SVM) was used to detect food images and to analyze the food balance. Experimental results show that the food image extraction presents above 88% of accuracy and the food balance estimation is achieved with more than 73% of accuracy.


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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D. Tancharoen, W. Puangpakisiri, T. Yamasaki, K. Aizawa, "Life log platform for continuous and discrete recording and retrieval of personal media," ECTI-EEC Transactions, vol.5, no.2, pp.165--173, August, 2007.
 
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mypyramid.gov, United States Department of Agriculture, http://www.mypyramid.gov/

Collaborative Colleagues:
Keigo Kitamura: colleagues
Toshihiko Yamasaki: colleagues
Kiyoharu Aizawa: colleagues