Role of Feature Selection on Leaf Image Classification
Affiliation(s)
1 Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur, India. 2 Department of Physics, Sir Padampat Singhania University, Udaipur, India. 3 College of Information Sciences and Technology, University of Nebraska, Omaha, NE, USA.
1 Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur, India. 2 Department of Physics, Sir Padampat Singhania University, Udaipur, India. 3 College of Information Sciences and Technology, University of Nebraska, Omaha, NE, USA.
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.
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.
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.
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.
References
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http://dx.doi.org/10.1007/978-1-4615-5689-3 [2] Blachnik, M., Duch, W., Kachel, A. and Biesiada, J. (2009) Feature Selection for Supervised Classification: A Kolmogorov-Smirnov Class Correlation-Based Filter. Proceedings of Methods of Artificial Intelligence, Gliwice, 10-19 November 2009, 33-40. [3] Hall, M.A. (1999) Correlation-Based Feature Selection for Machine Learning. PhD Thesis, University of Waikato, Hamilton. [4] Gonzalez, R.C. and Woods, R.E. (2001) Digital Image Processing. 2nd Edition, Prentice Hall, New Jersey. [5] Gabor Filters. https://en.wikipedia.org [6] Dins Lab, Random Forest. https://dinsdalelab.sdsu.edu/metag.stats/code/randomforest.html [7] R Development Core Team (2008) R: A language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. [8] Rasband, W.S. (1997-2014) ImageJ. U. S. National Institutes of Health, Bethesda. [9] Sim, J. and Wright, C.C. (2005) The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requi- rements. Physical Therapy, 85, 257-268. [10] Yu, W. and Chiu, D. (2015) Machine Learning with R Cookbook. 1st Edition, Packt Publishing Ltd., Birmingham.
[1] Liu, H. and Motoda, H. (1998) Feature Selection for Knowledge Discovery and Data Mining. 1st Edition, Kluwer Academic Publishers, New York.
http://dx.doi.org/10.1007/978-1-4615-5689-3 [2] Blachnik, M., Duch, W., Kachel, A. and Biesiada, J. (2009) Feature Selection for Supervised Classification: A Kolmogorov-Smirnov Class Correlation-Based Filter. Proceedings of Methods of Artificial Intelligence, Gliwice, 10-19 November 2009, 33-40. [3] Hall, M.A. (1999) Correlation-Based Feature Selection for Machine Learning. PhD Thesis, University of Waikato, Hamilton. [4] Gonzalez, R.C. and Woods, R.E. (2001) Digital Image Processing. 2nd Edition, Prentice Hall, New Jersey. [5] Gabor Filters. https://en.wikipedia.org [6] Dins Lab, Random Forest. https://dinsdalelab.sdsu.edu/metag.stats/code/randomforest.html [7] R Development Core Team (2008) R: A language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. [8] Rasband, W.S. (1997-2014) ImageJ. U. S. National Institutes of Health, Bethesda. [9] Sim, J. and Wright, C.C. (2005) The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requi- rements. Physical Therapy, 85, 257-268. [10] Yu, W. and Chiu, D. (2015) Machine Learning with R Cookbook. 1st Edition, Packt Publishing Ltd., Birmingham.