Role of Feature Selection on Leaf Image Classification

Abstract

The digital images have been studied for image classification, enhancement, image compression and image segmentation purposes. In the present work, it is proposed to study the effects of fea-ture selection algorithm on the predictive classification accuracy of algorithms used for discrimi-nating the different plant leaf images. The process involves extracting the important texture fea-tures from the digital images and then subjecting them to feature selection and further classifica-tion process. The leaf image features have been extracted by using Gabor texture features and these Gabor features are subjected to Random Forest feature selection algorithm for extracting impor-tant texture features. The four classification algorithms like K-Nearest Neighbour, J48, Classifica-tion and Regression Trees and Random Forest have been used for classification purpose. This study shows that there is a net improvement in the predictive classification accuracy values, when classification algorithms have been applied on selected features over the complete set of features.