Myunggeun Ji, Junchul Chun, Namgi Kim, Journal of Internet Computing and Services, Vol. 19, No. 3, pp. 35-42, Jun. 2018
10.7472/jksii.2018.19.3.35, Full Text:  HTML
Keywords: Batch Normalization, Convolutional Neural Network, Image Classification, Deep Learning

Abstract

Deep learning is known as a method of high accuracy among several methods for image classification. In this paper, we propose a method of enhancing the accuracy of image classification using CNN with a batch normalization method for classification of images using deep CNN (Convolutional Neural Network). In this paper, we propose a method to add a batch normalization layer to existing neural networks to enhance the accuracy of image classification. Batch normalization is a method to calculate and move the average and variance of each batch for reducing the deflection in each layer. In order to prove the superiority of the proposed method, Accuracy and mAP are measured by image classification experiments using five image data sets SHREC13, MNIST, SVHN, CIFAR-10, and CIFAR-100. Experimental results showed that the CNN with batch normalization is better classification accuracy and mAP rather than using the conventional CNN.

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