Comparison of Image Classification Techniques: Binary and Multiclass using Convolutional Neural Network and Support Vector Machines

Abstract

Classification is the technique applied in data mining to form groups under specified class labels. Classification is supervised type of machine learning.

In this paper, two popular classification techniques, Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are compared for accuracy of classification of images. Image classification is done on the basis of feature selection and feature extraction using Tensor flow package. The two classifiers understudy are Linear (SVM) and non-linear techniques (CNN). CNN possesses a powerful feature extraction and SVM is considered as a high-end classifier. Complexity of the feature extraction and selection can be increased in CNN by adding more layers, but in SVM complexity cannot be increased. CNN processes images using matrices of weights and is called as filters or features that detect specific attributes such as vertical edges, horizontal edges, etc. As and when the image progresses through each layer, the filters can recognize more and more complex attributes. In this proposed study, graphs of training phase of CNN also show how the training results are improved image by image due to increasing knowledge of features and thus loss is decreased.

This research study focuses on accuracy measure of the above mentioned methods. For the image classification studied in this paper, it has been observed that SVM gives adequate accuracy for binary classification whereas CNN gives consistent accuracy over binary as well as multi class classification problems.

The recognition rate achieved by the CNN algorithm varies between 75% - 75.40 % for binary and multiclass classification. SVM accuracy rate decreases from 80.95 % for binary classification, to 50 % for multiclass classification.