FoodLog: capture, analysis and retrieval of personal food images via web

Authors:
Keigo Kitamura;Toshihiko Yamasaki;Kiyoharu Aizawa
Affiliations:
The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan
Venue:
CEA '09 Proceedings of the ACM multimedia 2009 workshop on Multimedia for cooking and eating activities
Year:
2009

Citing 8
Cited 4

Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Performance Evaluation of Local Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Review: Which is the best way to organize/classify images by content?

Image and Vision Computing
Food log by analyzing food images

MM '08 Proceedings of the 16th ACM international conference on Multimedia
Coloring local feature extraction

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II

Image-based dietary information mining for community creation in a social network

Proceedings of second ACM SIGMM workshop on Social media
On integrating heterogeneous lifelog services

Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services
FoodBoard: surface contact imaging for food recognition

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Editorial: Designing for human-food interaction: An introduction to the special issue on 'food and interaction design'

International Journal of Human-Computer Studies

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the increase of the number of food images on the Internet, we have been developing a food-logging system which has an automated analysis function as a Web application. It can distinguish food images from other images, analyze the food balance, and visualize the log. In this paper, we demonstrate how the performance can be improved by the personalized models. Because our Web application has an interface to review and correct the food analysis results, the generation of the personalized models can be done on-line. Experimental results using two hundred images showed that the extracted image feature vectors differ from user to user but on the other hand the feature vectors and the food balance of each user have a strong correlation. Therefore, the accuracy of the food balance estimation was improved from 37% to 42% on average by the personalized classifier.