Leaf Classification Using Shape, Color, and Texture Features
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© July to Aug Issue 2011 by IJCTT Journal|
|Volume-1 Issue-3 |
|Year of Publication : 2011|
|Authors : Abdul Kadir, Lukito Edi Nugroho, Adhi Susanto, Paulus Insap Santosa.|
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 (IJCTT),V1(3):306-311 July to Aug Issue 2011 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: — Several methods to identify plants have been proposed by several researchers. Commonly, the methods did not capture color information, because color was not recognized as an important aspect to the identification. In this research, shape and vein, color, and texture features were incorporated to classify a leaf. In this case, a neural network called Probabilistic Neural network (PNN) was used as a classifier. The experimental result shows that the method for classification gives average accuracy of 93.75% when it was tested on Flavia dataset, that contains 32 kinds of plant leaves. It means that the method gives better performance compared to the original work.
 Q. Wu, C. Zhou, & C. Wang, “Feature Extraction and Automatic Recognition of Plant Leaf Using Artificial Neural Network”, Avances en Ciencias de la Computacion”, pp. 5-12, 2006.
 S. G. Wu, F. S. Bao, E. Y Xu, Y-X. Wang, Y-F. Chang, & Q-L. Xiang, “A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network”, IEEE 7th Interantional Symposium on Signal Processing and Information Technology, Cairo, 2007.
 K. Singh, I. Gupta, & S. Gupta, “SVM-BDT PNN and Fourier Moment Technique for Classification of Leaf Shape. International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 3 (4), pp. 67-78, 2010.
 J-X. Du, D-S. Huang, X-F. Wang, & X. Gu, ”Computer-aided Plant Species Identification (CAPSI) Based on Leaf Shape Matching Technique”, Transactions of the Institute of Measurement and Control , vol. 28 (3), pp. 275-284, 2006.
 P. Hiremath, & J. Pujari, “Content based Image Retrieval based on Color, Texture and Shape Features Using Image and Its Complement”, International Journal of Computer Science and Security , vol. 1 (4), pp. 44-50, 2011.
 B. Jyothi, Y. M. Latha, & V. Reddy, “Medical Image Retrieval using Multiple Features”, Advances in Computational Sciences and Technology , vol. 3 (3), pp. 387-396, 2010.
 S. Theodoridis, & K. Koutroumbas, “An Introduction Pattern Recognition”, Burlington: Academic Press, 2009.
 A. Kulkarni, “Artificial Neural Networks for Image Understanding”, New York: Van Nostrand Reinhold, 1994.
 M. Mercimek, K. Gulez, & T. V. Mumcu, “Real Object Recognition Using Moment Invariants”, Sadhana , vol. 30 (6), pp. 765-775, 2005
 Z. Zulkifli, “Plant Leaf Identification Using Moment Invariants & General Regression Neural Network”, Master Thesis, Universiti Teknologi Malaysia, 2009.
 A. Kadir, L.E. Nugroho, A. Susanto, & P.I. Santosa, “A Comparative Experiment of Several Shape Methods in Recognizing Plants”, International Journal of Computer Science & Information Technology (IJSIT) , vol. 3 (3), pp. 256-263.
 D. Zhang, “Image Retrieval Based on Shape”, Unpublished Dissertation, Monash University, 2002.
 I. Kunttu, L. Lepisto, & A. Visa, “Image Correlogram in Image Database Indexing and Retrieval”, Proceedings of 4th European Workshop on Image Analysis for Multimedia Interaction Services, pp. 88-91, London, 2003.
 Q-K. Man, C-H. Zheng, X-F. Wang, & F-Y.Lin,”Recognition of Plant Leaves Using Support Vector”, International Conference on Intelligent Computing, pp. 192-199, Shanghai, 2008.
 R. S. Choras, “Image Feature Extraction Techniques and Their Application for CBIR and Biometrics Systems”, International Journal of Biology and Biomedical Engineering , vol. 1 (1), pp. 6-16, 2007.
 T. Acharya, & A.K. Ray, “Image Processingg Principles and Applications”, New Jersey: John Wiley & Sons, Inc, 2005.
 Y.A.O. Min, Y.I. Win-Sheng,S. Bin, &D.A.I. Hong-Hua, “An Image Retrieval System Based on Fractal Dimension”, Journal Zheijang University Science, vol. 4(4), p. 421-425, 2003.
 M. Petrou, & P.G. Sevilla, “Image Processing Dealing with Texture”, Chichester: John Wiley & Sons, Ltd., 2006.
 F. Jing, R. Bhuvaneswaran, Y. Katayama, & N. Takahashi, “Adaptive Route Selection Policy Based on Back Propagation Neural Networks”, Journal of Networks , vol. 3 (3), pp. 34-41, 2008.
 L. Gang, “Comparative reseraches on Probabilistic Neural Networks and Multi-layer Perceptron Networks for Remote Sensing Image Segmentation”, International Conference on Geo-spatial Solutions for Emergency Management, pp. 25-29, Beijing, 2009.
 P. Burrascano, E. Cardelli, A. Faba, S. Fiori, & A. Massinelli. “Application of probabilistic Neural Networks to Eddy Current Non Destructive Test Problems”, in Proceedings of 7th International Conference on Engineering Applications of Neural Networks, 2010, Cagliari.
 M.S. Nixon, &A.S. Aguado, “Feature Extraction and Image Processing”, Woburn: Newnes, 2002.
 A.Eleyan, & H. Demirel, “Co-occurrence Matrix and Its Statistical Features as a New Approach for Face Recognition. Turkish Journal Of Electrical Engineering & Computer Sciences , vol. 19 (1), pp. 97-107, 2011.
 S. Newsam, & C. Kamath, “Comparing Shape and Texture Features for Pattern Recognition in Simulation Data”, IS&T/SPIE`s Annual Symposium on Electronic Imaging. San Jose, 2005.
 R. Dobrescu, M. Dobrescu, S. Mocanu, & D. Popescu, “Medical Images Classification for Skin Cancer Diagnosis Based on Combined Texture and Fractal Analysis. WISEAS Transactions on Biology and Biomedicine , vol. 7 (3), pp. 223-232, 2010.
 Y. Zhang, & R.Wang,. “Neural Network Combines with a Rotational Invariant Feature Set”, 8th Pacific Rim International Conference on Artificial Intelligence, pp. 436-444, Aucland, 2004.
 K. Pahalawatta, “Plant Species Biometric Using Features Hierarchies A Plant Identification System Using Both Global and Local Features of Plant Leave”, Master Thesis, University of Canterburry, 2008.
 V. Cheung, & K. Cannons. (2003) An Introduction to Probabilistic Neural Networks. [Online]. Available: http://www.psi.toronto.edu/~vincent/research/presentations/PNN.pdf
 I. M. Emary, & S. Ramakhrisnan, “On the Application of Various Probabilistic Neural Networks in Solving Different Pattern Classification Problems”, World Applied Sciences Journal , vol. 4 (6), 772-780, 2008.
KeywordsColor features, Foliage plants, Lacunarity, Leaf classification, PFT, PNN, Texture features.