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dc.contributor.authorHe, Y.
dc.contributor.authorXu, C.
dc.contributor.authorKhanna, N.
dc.contributor.authorBoushey, Carol
dc.contributor.authorDelp, E.
dc.identifier.citationHe, Y. and Xu, C. and Khanna, N. and Boushey, C. and Delp, E. 2013. Context based food image analysis, in Proceedings of the 20th International Conference on Image Processing, Sep 15-18 2013, pp. 2748-2752. Melbourne, VIC, Australia: IEEE.

We are developing a dietary assessment system that records daily food intake through the use of food images. Recognizing food in an image is difficult due to large visual variance with respect to eating or preparation conditions. This task becomes even more challenging when different foods have similar visual appearance. In this paper we propose to incorporate two types of contextual dietary information, food co-occurrence patterns and personalized learning models, in food image analysis to reduce ambiguity in food visual appearance and improve food recognition accuracy. We evaluate our model on 1453 food images acquired by 45 participants in natural eating conditions. The result shows that incorporating contextual dietary information improves the food categorization accuracy by about 10%.

dc.titleContext based food image analysis
dc.typeConference Paper
dcterms.source.title2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
dcterms.source.series2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
curtin.departmentSchool of Public Health
curtin.accessStatusFulltext not available

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