CogKnife: Food recognition from their cutting sounds

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

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781509015528
DOI
出版ステータスPublished - 2016 9 22
外部発表はい
イベント2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 - Seattle, United States
継続期間: 2016 7 112016 7 15

出版物シリーズ

名前2016 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

ASJC Scopus subject areas

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

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