Architectures for Image Classification and Performance Analysis Using Different Convolution Neural Network and Deep Neural Networks and Different Combinations of Activation Functions
Rathinasabapathy Ramadevi · SSRN Electronic Journal · 2020
Image classification is a complex computer vision problem. Recently, there have been endeavors using convolutional and deep neural network architectures (CNNDNN). There are very deep neural networks architectures available for image classification. These networks are taking days and even weeks to learn the network. It will be time consuming if it is to be tested in various ways to find an efficient architecture. It gives rise to shallow neural networks which are having few layers. Due to this fact, such types of neural network architectures have been implemented in this research endeavor. Recently, several other improvements have been made on CNNs by way of introducing several types of convolutions and different types of activation functions. The shallow network makes it possible to test these different types of networks and finding the efficient network architecture given the less amount of time to learn in such type of network architectures. In this research five different CNN-DNN architectures have been analyzed on an image set having fifteen categories of images and their results were compared. The architectures were varied by image sizes taken, different number of filters, different sub-sampling processes and different activation functions. It is found that an architecture with a combination of Relu and Hyperbolic activation functions were performing well over other architectures and other combinations of activation functions.