Open Science Research Excellence

Open Science Index

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


Select areas to restrict search in scientific publication database:
2535
An Optimal Feature Subset Selection for Leaf Analysis
Abstract:
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.
Digital Object Identifier (DOI):

References:

[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.
[2] Abdul Kadir, Lukito Edi Nugroho, Adhi Susanto, Paulus Insap Santosa, Leaf Classification Using Shape, Color, and Texture Features, International Journal of Computer Trends and Technology, P.224- 230,2011.
[3] Jyotismita Chaki, Ranjan Parekh, Plant Leaf Recognition using Shape based Features and Neural Network classifiers, International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 2, No. 10, P. 41-47, 2011.
[4] Wahyu Wibowo, Hugh E. Williams, Simple and Accurate Feature Selection for Hierarchical Categorisation, ACM Digital library, 2002.
[5] P. Tzionas, S.E. Papadakis, and D. Manolakis, "Plant leaves classification based on morphological features and a fuzzy surface selection technique", in Proceeding of International Conference on Technology and Automation, Thessaloniki, Greece, P. 365-370, 2005.
[6] Xiaodong Zheng, Xiaojie Wang, Leaf Vein Extraction Based on Grayscale Morphology, I.J. Image, Graphics and Signal Processing, Vol.2, 2P.25-31,2010.
[7] N. Kumar, S. Pandey, A. Bhattacharya, and P. S. Ahuja, "Do leaf surface characteristics affect agrobacterium infection in tea J. Biosci., vol. 29, no. 3, P. 309-317, 2004.
[8] G. Guo, S. Li, and K. Chan, "Support vector machines for face recognition," Image and Vision Computing, vol. 19, no. 9, P. 631-638, 2001.
[9] S. Papadakis, P. Tzionas, V. Kaburlazos, and J. Theocharis, "A genetic based approach to the Type I structure identification problem," Informatica, vol. 5, no. 3, 2005.
[10] Yan Li, Zheru Chi, and David D. Feng, "Leaf Vein Extraction Using Independent Component Analysis," 2006 IEEE Conference on Systems, Man and Cybernetics, Vol. 5, Taipei, P. 3890-3894,2006.
[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 et.al., 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.
[14] R. Sinan Tumen1, M. Emre Acer2 and T. Metin Sezgin1, Feature Extraction and Classifier Combination for Image-based Sketch Recognition, EUROGRAPHICS Symposium on Sketch-Based Interfaces and Modeling , P.1-8,2010.
[15] Chomtip Pornpanomchai, Supolgaj Rimdusit, Piyawan Tanasap and Chutpong Chaiyod, Thai Herb Leaf Image Recognition System (THLIRS), Kasetsart J. (Nat. Sci.) , Vol.45, P. 551 - 562 ,2011.
[16] Krzyszt Michalak, Halina Kwasnicka, Correlation-Based Feature Selection Strategy in Classification Problems, Int. J. Appl. Math. Comput. Sci., Vol. 16, No. 4, P.503-511, 2006.
[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.
[22] Yijuan Lu, Ira Cohen, Xiang Sean Zhou, Qi Tian, Feature Selection Using Principal Feature Analysis, ACM Multimedia, September 23-29, 2007.
[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.
Vol:13 No:03 2019Vol:13 No:02 2019Vol:13 No:01 2019
Vol:12 No:12 2018Vol:12 No:11 2018Vol:12 No:10 2018Vol:12 No:09 2018Vol:12 No:08 2018Vol:12 No:07 2018Vol:12 No:06 2018Vol:12 No:05 2018Vol:12 No:04 2018Vol:12 No:03 2018Vol:12 No:02 2018Vol:12 No:01 2018
Vol:11 No:12 2017Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007