Impact of Automatic Feature Extraction in Deep Learning Architecture

Abstract

This paper presents the impact of automatic feature extraction used in a deep learning architecture such as Convolutional Neural Network (CNN). Recently CNN has become a very popular tool for image classification which can automatically extract features, learn and classify them. It is a common belief that CNN can always perform better than other well-known classifiers. However, there is no systematic study which shows that automatic feature extraction in CNN is any better than other simple feature extraction techniques, and there is no study which shows that other simple neural network architectures cannot achieve same accuracy as CNN. In this paper, a systematic study to investigate CNN's feature extraction is presented. CNN with automatic feature extraction is firstly evaluated on a number of benchmark datasets and then a simple traditional Multi-Layer Perceptron (MLP) with full image, and manual feature extraction are evaluated on the same benchmark datasets. The purpose is to see whether feature extraction in CNN performs any better than a simple feature with MLP and full image with MLP. Many experiments were systematically conducted by varying number of epochs and hidden neurons. The experimental results revealed that traditional MLP with suitable parameters can perform as good as CNN or better in certain cases