Abstract
We have developed a dietary assessment system that uses food images captured by a mobile device. Food identification is a crucial component of our system. Achieving a high classification rates is challenging due to the large number of food categories and variability in food appearance. In this paper, we propose to improve food classification by incorporating temporal information. We employ recursive Bayesian estimation to incrementally learn from a person’s eating history. We show an improvement of food classification accuracy by 11% can be achieved.
E. Delp—This work was sponsored by grant from the National Institutes of Health under grant NIEH/NIH 2R01ES012459-06. Address all correspondence to E. J. Delp: ace@ecn.purdue.edu or see www.tadaproject.org.
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Wang, Y., He, Y., Zhu, F., Boushey, C., Delp, E. (2015). The Use of Temporal Information in Food Image Analysis. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science(), vol 9281. Springer, Cham. https://doi.org/10.1007/978-3-319-23222-5_39
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DOI: https://doi.org/10.1007/978-3-319-23222-5_39
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