  The pervasiveness of mobile cameras has resulted in a dramatic increase in
food photos, which are pictures reflecting what people eat. In this paper, we
study how taking 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 additional 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 demonstrate image-based
recognition of foods eaten in restaurants by training a classifier with images
from restaurant's online menu databases. We evaluate the performance of our
system in unconstrained, real-world settings with food images taken in 10
restaurants across 5 different types of food (American, Indian, Italian,
Mexican and Thai).
