A Comparative Approach on Classification of Images with Convolutional Neural Networks

Abstract

Image degradation, such as blurring, or various sources of noise are common reasons for distortion happening during image procurement. In this paper, we will study in a systematical manner the efficiency of various Convolutional Neural Networks (CNN) approaches, in respects to the type of architecture and optimization strategies, with two main objectives in mind. Firstly, we examine the CNN performance in classifying clean images, with a dataset containing 8 classes and more than 18,000 images, observing comparatively the obtained results from training on a standard architecture with those obtained from training on a hyper parameters fine-tuned network and lastly, from training on a wider pre fine-tuned network. Secondly, training our model after a degradation function is applied, and after analyzing the results, we propose an approach which will gently balance the efforts stemming from difficult architecture de-sign or adopting the best optimization decisions with obtaining a satisfactory efficiency in a simple manner. We have offered a standard convolution architecture as a solution for classifying images which are distorted, and our results suggest that, departing from a simple design, with possible alterations of hyper parameters and other optimizing routes, the efficiency could massively increase.