TY - GEN
T1 - Food classification from images using convolutional neural networks
AU - Attokaren, David J.
AU - Fernandes, Ian G.
AU - Sriram, A.
AU - Murthy, Y. V.Srinivasa
AU - Koolagudi, Shashidhar G.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/12/19
Y1 - 2017/12/19
N2 - The process of identifying food items from an image is quite an interesting field with various applications. Since food monitoring plays a leading role in health-related problems, it is becoming more essential in our day-to-day lives. In this paper, an approach has been presented to classify images of food using convolutional neural networks. Unlike the traditional artificial neural networks, convolutional neural networks have the capability of estimating the score function directly from image pixels. A 2D convolution layer has been utilised which creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. There are multiple such layers, and the outputs are concatenated at parts to form the final tensor of outputs. We also use the Max-Pooling function for the data, and the features extracted from this function are used to train the network. An accuracy of 86.97% for the classes of the FOOD-101 dataset is recognised using the proposed implementation.
AB - The process of identifying food items from an image is quite an interesting field with various applications. Since food monitoring plays a leading role in health-related problems, it is becoming more essential in our day-to-day lives. In this paper, an approach has been presented to classify images of food using convolutional neural networks. Unlike the traditional artificial neural networks, convolutional neural networks have the capability of estimating the score function directly from image pixels. A 2D convolution layer has been utilised which creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. There are multiple such layers, and the outputs are concatenated at parts to form the final tensor of outputs. We also use the Max-Pooling function for the data, and the features extracted from this function are used to train the network. An accuracy of 86.97% for the classes of the FOOD-101 dataset is recognised using the proposed implementation.
UR - https://www.scopus.com/pages/publications/85044212352
UR - https://www.scopus.com/pages/publications/85044212352#tab=citedBy
U2 - 10.1109/TENCON.2017.8228338
DO - 10.1109/TENCON.2017.8228338
M3 - Conference contribution
AN - SCOPUS:85044212352
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 2801
EP - 2806
BT - TENCON 2017 - 2017 IEEE Region 10 Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Region 10 Conference, TENCON 2017
Y2 - 5 November 2017 through 8 November 2017
ER -