Leveraging Context to Support Automated Food Recognition in Restaurants

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 conte...

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Bibliographic Details
Main Authors: Bettadapura, Vinay, Thomaz, Edison, Parnami, Aman, Abowd, Gregory D., Essa, Irfan
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Feature extraction
Google
Image color analysis
Image recognition
Image segmentation
Training
Online Access:Request full text
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Summary: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).
ISSN:1550-5790
2642-9381
DOI:10.1109/WACV.2015.83