Paper in IEEE WACV (2015): "Leveraging Context to Support Automated Food Recognition in Restaurants"

Paper

V. Bettadapura, E. Thomaz, Aman Parnami, G. Abowd, and I. Essa (2015), “Leveraging Context to Support Automated Food Recognition in Restaurants,” in IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. [PDF] [WEBSITE] [DOI] [arXiv] [BIBTEX]

@InProceedings{ 2015-Bettadapura-LCSAFRR,
arxiv = {http://arxiv.org/abs/1510.02078},
author  = {Vinay Bettadapura and Edison Thomaz and Aman
Parnami and Gregory Abowd and Irfan Essa},
booktitle  = {{IEEE Winter Conference on Applications of Computer
Vision (WACV)}},
doi = {10.1109/WACV.2015.83},
month = {January},
pdf = {http://www.cc.gatech.edu/~irfan/p/2015-Bettadapura-LCSAFRR.pdf},
publisher  = {IEEE Computer Society},
title = {Leveraging Context to Support Automated Food
Recognition in Restaurants},
url = {http://www.vbettadapura.com/egocentric/food/},
year = {2015}
}

Abstract

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).