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Classifying multi-category images using deep learning : A convolutional neural network model
Ardhendu Bandhu,
Published in IEEE
Volume: 2018-January
Pages: 915 - 919
This paper presents an image classification model using a convolutional neural network with Tensor Flow. Tensor Flow is a popular open source library for machine learning and deep neural networks. A multi-category image dataset has been considered for the classification. Conventional back propagation neural network has an input layer, hidden layer, and an output layer but convolutional neural network, has a convolutional layer, and a max pooling layer. We train this proposed classifier to calculate the decision boundary of the image dataset. The data in the real world is mostly in the form of unlabeled and unstructured format. These unstructured data may be image, sound and text data. Useful information cannot be easily derived from neural networks which are shallow i.e. the ones which have less number of hidden layers. We propose deep neural network based CNN classifier which has a large number of hidden layers and can derive meaningful information from images. © 2017 IEEE.
About the journal
JournalData powered by Typeset2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT)
PublisherData powered by TypesetIEEE
Open Access0