Role of Feature Selection on Leaf Image Classification

Poonam  Saini; Arun  Kumar; Vinod  Patidar; Deepak  Khazanchi

doi:10.4236/jdaip.2015.34018

Arun Kumar¹, Vinod Patidar², Deepak Khazanchi³, Poonam Saini¹

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 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.

Keywords: Leaf Image, Feature Selection Algorithm, Random Forest, Gabor Texture Features

Cite this paper: Kumar, A. , Patidar, V. , Khazanchi, D. and Saini, P. (2015) Role of Feature Selection on Leaf Image Classification. Journal of Data Analysis and Information Processing, 3, 175-183. doi: 10.4236/jdaip.2015.34018.

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