Title:
Leveraging Context to Support Automated Food Recognition in Restaurants
Leveraging Context to Support Automated Food Recognition in Restaurants
Author(s)
Bettadapura, Vinay
Thomaz, Edison
Parnam, Aman
Abowd, Gregory D.
Essa, Irfan
Thomaz, Edison
Parnam, Aman
Abowd, Gregory D.
Essa, Irfan
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Abstract
The pervasiveness of mobile cameras has resulted in a
dramatic increase in food photos, which are pictures re-
flecting what people eat. In this paper, we study how tak-
ing pictures of what we eat in restaurants can be used for
the purpose of automating food journaling. We propose to
leverage the context of where the picture was taken, with ad-
ditional information about the restaurant, available online,
coupled with state-of-the-art computer vision techniques to
recognize the food being consumed. To this end, we demon-
strate image-based recognition of foods eaten in restaurants
by training a classifier with images from restaurant’s on-
line menu databases. We evaluate the performance of our
system in unconstrained, real-world settings with food im-
ages taken in 10 restaurants across 5 different types of food
(American, Indian, Italian, Mexican and Thai).
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Date Issued
2015-01
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Text
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Post-print
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Proceedings