Deep Learnt Features and Machine Learning Classifier for Texture classification

Abstract

Texture classification plays a vital role in the emerging research field of image classification. This paper approaches the texture classification problem using significant features extracted from pre-trained Convolutional Neural Network (CNN) like Alexnet, VGG16, Resnet18, Googlenet, MobilenetV2, and Darknet19. These features are classified by machine learning classifiers such as Support Vector Machine (SVM), Ensemble, K Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), and Discriminant Analysis (DA). The performance of the work is evaluated with the texture databases namely KTH-TIPS, FMD, UMD-HR, and DTD. Among these CNN features derived from VGG16 classify by SVM provides better classification accuracy rather than using VGG16 with a softmax classifier.