Fast food recognition from videos of eating for calorie estimation

Authors:
Wen Wu;Jie Yang
Affiliations:
Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
Venue:
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Year:
2009

Citing 3
Cited 4

Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Image Based Localization in Urban Environments

3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)

Drinking activity analysis from fast food eating video using generative models

CEA '09 Proceedings of the ACM multimedia 2009 workshop on Multimedia for cooking and eating activities
PFID: pittsburgh fast-food image dataset

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Summary abstract for the 1st ACM international workshop on personal data meets distributed multimedia

Proceedings of the 21st ACM international conference on Multimedia
Situation fencing: making geo-fencing personal and dynamic

Proceedings of the 1st ACM international workshop on Personal data meets distributed multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

Accurate and passive acquisition of dietary data from patients is essential for a better understanding of the etiology of obesity and development of effective weight management programs. Self-reporting is currently the main method for such data acquisition. However, studies have shown that data obtained by self-reporting seriously underestimate food intake and thus do not accurately reflect the real habitual behavior of individuals. Computer food recognition programs have not yet been developed. In this paper, we present a study for recognizing foods from videos of eating, which are directly recorded in restaurants by a web camera. From recognition results, our method then estimates food calories of intake. We have evaluated our method on a database of 101 foods from 9 food restaurants in USA and obtained promising results.