Classification of Image using Convolutional Neural Network (CNN)

Abstract

Computer vision is concerned with the automatic extraction analysis and understanding of useful information from a single image or a sequence of images We have used Convolutional Neural Networks CNN in automatic image classification systems In most cases we utilize the features from the top layer of the CNN for classification however those features may not contain enough useful information to predict an image correctly In some cases features from the lower layer carry more discriminative power than those from the top Therefore applying features from a specific layer only to classification seems to be a process that does not utilize learned CNN s potential discriminant power to its full extent Because of this property we are in need of fusion of features from multiple layers We want to create a model with multiple layers that will be able to recognize and classify the images We want to complete our model by using the concepts of Convolutional Neural Network and CIFAR-10 dataset Moreover we will show how MatConvNet can be used to implement our model with CPU training as well as less training time The objective of our work is to learn and practically apply the concepts of Convolutional Neural Network