Comparative Analysis of Image Classification Algorithms

Abstract

Abstract: Image classification is a hot research topic in today's society and an important direction in the field of image processing research. Image classification is a supervised learning method used to classify images. Paper analyses four common image classification algorithms: convolution neural network, support vector machine, artificial neural network and logistic regression. In the research work, both theoretical and empirical approaches were followed. For the theoretical approach a review of both secondary data as well as data based on results obtained by application on the tools is studied. Secondary data was acquired from the research articles, text books, journals, technical reports, published thesis, websites, e-journals, software tool manuals, conference proceedings and any other research articles published in the related domain. The empirical study was carried out on the set of experiments, using software tools. The results obtained from the experiments were analyzed for the finding of the research. The paper compares the results of these four algorithms when tested on same dataset, in same environment and on same system. Research paper proves that results obtained from theoretical analysis are same as results obtained from experiments. The study found out that best results were given by convolution neural network, followed by support vector machine, artificial neural network and at last logistic regression.