Food balance estimation by using personal dietary tendencies in a multimedia food log

IEEE Transactions on Multimedia

Volumn 15, Issue 8, 2013, Pages 2176-2185

  Aizawa, Kiyoharu ^a   Maruyama, Yuto ^b   Li, He ^a   Morikawa, Chamin ^d   De Silva, G C ^c  

^a UNIVERSITY OF TOKYO   (Japan)

^b HITACHI LTD   (Japan)

^c Motion Portrait Inc   (Japan)

^d NONE

Author keywords

Bayesian estimation; Food log; Food record; Image processing; Lifelog; Multimedia

Indexed keywords

BALANCE ESTIMATION; BAYESIAN ESTIMATIONS; BAYESIAN FRAMEWORKS; INCREMENTAL LEARNING; LIFE LOG; MEAN ABSOLUTE ERROR; MULTIMEDIA; PRIOR DISTRIBUTION;

BAYESIAN NETWORKS; IMAGE PROCESSING; IMAGE RECORDING;

ESTIMATION;

EID: 84888357141     PISSN: 15209210     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMM.2013.2271474     Document Type: Article

References (23)

1. F. E. Thompson, A. F. Subar, C. M. Loria, J. L. Reedy, and T. Baranowski, "Need for technological innovation in dietary assessment," J. Amer. Diet. Assoc., vol. 110, no. 1, pp. 48-51, 2010.
2. C. K. Martin, H. Han, S. M. Coulon, H. R. Allen, C.M. Champagne, and S. D. Anton, "A novel method to remotely measure food intake of free-living individuals in real time: The remote food photography method," British J. Nutrition, vol. 101, pp. 446-456, 2009.
3. N. Khanna, C. J. Boushey, D. Kerr, M. Okos, D. S. Ebert, and E. J. Delp, "An overview of the technology assisted dietary assessment project at Purdue University," in Proc. IEEE ISM, 2010, pp. 290-295.
4. L. Zepeda and D. Deal, "Think before you eat: Photographic food diaries as intervention tools to change dietary decision making and attitudes," Int. J. Consum. Studies, vol. 32, no. 6, pp. 692-698, 2008.
5. C. K. Martin, J. B. Correa, H. Han, H. R. Allen, J. C. Rood, C. M. Champagne, B. K. Gunturk, and G. A. Bray, "Validity of the remote food photography method (RFPM) for estimating energy and nutrient intake in near real-time," Obesity, vol. 20, no. 4, pp. 891-899, 2012.
6. J. Noronha, E. Hysen, H. Zhang, and K. Z. Gajos, "Plate Mate: Crowdsourcing nutrition analysis from food photographs," in Proc. ACM UIST, 2011, pp. 1-12.
7. K. Kitamura, T. Yamasaki, and K. Aizawa, "Food Log by analyzing food images," in Proc. ACM Multimedia, 2008, pp. 999-1000.
8. K. Aizawa, "Food Log and future prospect of life log," (in Japanese) J. IPSJ, vol. 50, no. 7, pp. 592-597, 2009.
9. T. Joutou and K. Yanai, "A food image recognition system with multiple kernel learning," in Proc. IEEE ICIP, 2009, pp. 285-288.
10. F. Zhu, M. Bosch, I. Woo, S.-Y. Kim, C. J. Boushey, D. S. Ebert, and E. J. Delp, "The use of mobile devices in aiding dietary assessment and evaluation," IEEE J. Select. Topics Signal Process., vol. 4, no. 4, pp. 756-766, 2010.
11. W. Wu and J. Yang, "Fast food recognition from videos of eating for calorie estimation," in Proc. IEEE ICME, 2009, pp. 1210-1213.
12. S. Yang, M. Chen, D. Pomerleau, and R. Sukthankar, "Food recognition using statistics of pairwise local features," in Proc. IEEE CVPR, 2010, pp. 2249-2256.
13. M. Bosch, F. Zhu, N. Khanna, C. J. Boushey, and E. Delp, "Combining global and local features for food identification in dietary assessment," in Proc. IEEE ICIP, 2011, pp. 1789-1792.
14. T. Miyazaki, G. C. de Silva, and K. Aizawa, "Image-based calorie content estimation for dietary assessment," in Proc. IEEE ISM, Workshop Multimedia for Cooking and Eating Activities, 2011, pp. 363-368.
15. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vision, vol. 60, no. 2, pp. 91-110, 2004.
16. G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, "Visual categorization with bags of keypoints," in Proc. ECCV, Workshop Statistical Learning in Computer Vision, 2004, pp. 59-74.
17. Y. Freund and R. E. Schapire, "Experiments with a new boosting algorithm," in Proc. Conf. Learning Theory, 1996, pp. 209-217.
18. C.-C. Chang and C.-J. Lin, "LIBSVM: A Library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, 2011.
19. R. J. Schalkoff, Pattern Recognition: Statistical, Structural, and Neural Approaches. New York, NY, USA: Wiley, 1992.
20. Ministry of Agriculture, Forestry and Fisheries of Japan, Food Balance Guide [Online]. Available: http://www.maff.go.jp/j/balance-guide/ (in Japanese)
21. United States Department of Agriculture, MyPlate & Food Pyramid Resources [Online]. Available: http://fnic.nal.usda.gov/dietary-guidance/
22. V. Messina, V. Melina, and A. R. Mangels, "A new food guide for North American vegetarians," Canadian J. Diet. Practice Res., vol. 64, no. 2, pp. 82-86, 2003. (Pubitemid 37046624)
23. B. Yao, G. Bradski, and L. Fei-Fei, "A codebook-free and annotationfree approach for fine grained image categorization," in Proc. IEEE CVPR2012, 2012, pp. 3466-3473.