CogKnife: Food recognition from their cutting sounds

Takamichi Kojima, Takashi Ijiri, Jeremy White, Hidetomo Kataoka, Akira Hirabayashi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

In this study, we present 'CogKnife', a knife device which can identify food. For this, a small microphone is attached to a knife, which records the cutting sound of food. We extract spectrograms from the cutting sounds and use them as feature vectors to train a classifier. This study used the k-Nearest Neighbor method (k-NN), the support vector machine (SVM) and the convolutional neural network (CNN) to verify differences of the classification methods. To evaluate the accuracy of our technique, we performed classification experiments with six kinds of foods (apples, bananas, cabbages, leeks and peppers) in a laboratory environment. From 20-fold cross validation, we confirmed high recognition accuracies, such as 83% with k-NN, 95% with SVM and 89% with CNN.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509015528
DOIs
Publication statusPublished - 2016 Sep 22
Event2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 - Seattle, United States
Duration: 2016 Jul 112016 Jul 15

Publication series

Name2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016

Other

Other2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
CountryUnited States
CitySeattle
Period16/7/1116/7/15

Keywords

  • Cooking support
  • Food recognition
  • Machine learning
  • Pattern recognition
  • Sound recognition

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition

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