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dc.contributor.authorAttokaren, D.J.
dc.contributor.authorFernandes, I.G.
dc.contributor.authorSriram, A.
dc.contributor.authorMurthy, Y.V.S.
dc.contributor.authorKoolagudi, S.G.
dc.identifier.citationIEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, Vol.2017-December, , pp.2801-2806en_US
dc.description.abstractThe 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. � 2017 IEEE.en_US
dc.titleFood classification from images using convolutional neural networksen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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