Enhancing Convolutional Neural Network using Hu’s Moments

Abstract

Convolutional Neural Networks (CNN) is a powerful deep learning method which is mostly used in image classification and image recognition applications. It has achieved acceptable accuracy in these fields but it still suffers some limitations. One of these limitations of CNN is the lack of ability to be invariant to the input data due to some transformations such as rotation, scaling, skewness, etc. In this paper we present an approach to optimize CNN in order to enhance its performance regarding the invariant limitation by using Hu’s moments. The Hu’s moments of an image are weighted averages of the image’s intensities of the pixels, which produce statistics about the image, and these moments are invariant to image transformations. This means that, even if some changes were made to the image, it will always produce almost the same moments values. The main idea behind the proposed approach is extracting Hu’s moments of the image and concatenating them with the flatten vector then feeding the new vector to the fully connected layer. The experimental results show that an acceptable loss, accuracy, precision, recall and F1 score have been achieved on three benchmark datasets which are MNIST hand written digits dataset, MNIST fashion dataset and the CIFAR10 dataset.