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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29414

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An Optimal Feature Subset Selection for Leaf Analysis
This paper describes an optimal approach for feature subset selection to classify the leaves based on Genetic Algorithm (GA) and Kernel Based Principle Component Analysis (KPCA). Due to high complexity in the selection of the optimal features, the classification has become a critical task to analyse the leaf image data. Initially the shape, texture and colour features are extracted from the leaf images. These extracted features are optimized through the separate functioning of GA and KPCA. This approach performs an intersection operation over the subsets obtained from the optimization process. Finally, the most common matching subset is forwarded to train the Support Vector Machine (SVM). Our experimental results successfully prove that the application of GA and KPCA for feature subset selection using SVM as a classifier is computationally effective and improves the accuracy of the classifier.
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[1] A.Kadir,L.E. Nugroho, A. Susanto and P.I. Santosa, A Comparative Experiment of Several Shape Methods in Recognizing Plants, International Journal of Computer Science and Information Technology (IJCSIT), Vol 3, No 3, P.256-263, June 2011.
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[11] Chomtip Pornpanomchai, Chawin Kuakiatngam Pitchayuk Supapattranon, and Nititat Siriwisesokul,, Leaf and Flower Recognition System (e-Botanist), International Journal of Engineering and Technology (IACSIT), Vol.3, No.4, ,P.347-351, 2011.
[12] B.Sathya Bama, Content Based Leaf Image Retrieval (CBLIR) Using Shape, Color and Texture Features, Indian Journal of Computer Science and Engineering (IJCSE), Vol. 2 ,No. 2 ,P. 202-211,2011.
[13] Maliheh Shabanzade, Morteza Zahedi and Seyyed Amin Aghvami, Combination of Local Descriptors and Global Features for Leaf Recognition, Signal and Image Processing : An International Journal (SIPIJ) Vol.2, No.3, P. 23-31,2011.
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[17] Qisong Chen, Xiaowei Chen and Yun Wu, Optimization Algorithm with Kernel PCA to Support Vector Machines for Time Series Prediction, Journal of Computers, Vol. 5, NO. 3, P.380-387, 2010.
[18] Shanwen Zhang and Kwok-Wing Chau, Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification, ICIC 2009, LNCS 5754, P. 948-955, 2009.
[19] Debdoot Sheet and Jyotirmoy Chatterjee, Hrushikesh Garud, Feature Usability Index and Optimal Feature Subset Selection, International Journal of Computer Applications, Vol.12, No.2, P.29-37, 2010.
[20] D S Guru, Y. H. Sharath, S. Manjunath, Texture Features and KNN in Classification of Flower Images, IJCA Special Issue on "Recent Trends in Image Processing and Pattern Recognition", P.21-29, 2010.
[21] Minh Hoai Nguyen, Fernando De la Torre, Optimal Feature Selection for Support Vector Machines, Pattern Recoginition, P. 1-25, 2009.
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[23] Amaro Lima, Heiga Zen, Yoshihiko, Keiichi Tokuda,Tadashi Kitamura, Members, and Fernando G. Resende, Applying Sparse KPCA for Feature Extraction in Speech Recognition, IEICE TRANS. INF. & SYST., Vol.E88-D, No.3, P. 401-402, 2010.
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