Role of Feature Selection on Leaf Image Classification

作     者：Arun Kumar Vinod Patidar Deepak Khazanchi Poonam Saini 

作者机构：Department of Computer Science and Engineering Sir Padampat Singhania University Udaipur India Department of Physics Sir Padampat Singhania University Udaipur India College of Information Sciences and Technology University of Nebraska Omaha NE USA 

出 版 物：《Journal of Data Analysis and Information Processing》 (数据分析和信息处理（英文）)

年 卷 期：2015年第3卷第4期

页      面：175-183页

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Leaf Image Feature Selection Algorithm Random Forest Gabor Texture Features 

摘      要：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 feature selection algorithm on the predictive classification accuracy of algorithms used for discriminating the different plant leaf images. The process involves extracting the important texture features from the digital images and then subjecting them to feature selection and further classification 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 important texture features. The four classification algorithms like K-Nearest Neighbour, J48, Classification 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.