TY - GEN
T1 - CogKnife
T2 - 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
AU - Kojima, Takamichi
AU - Ijiri, Takashi
AU - White, Jeremy
AU - Kataoka, Hidetomo
AU - Hirabayashi, Akira
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/22
Y1 - 2016/9/22
N2 - 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.
AB - 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.
KW - Cooking support
KW - Food recognition
KW - Machine learning
KW - Pattern recognition
KW - Sound recognition
UR - http://www.scopus.com/inward/record.url?scp=84992129694&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992129694&partnerID=8YFLogxK
U2 - 10.1109/ICMEW.2016.7574741
DO - 10.1109/ICMEW.2016.7574741
M3 - Conference contribution
AN - SCOPUS:84992129694
T3 - 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
BT - 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 July 2016 through 15 July 2016
ER -