International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.5, No.9, Jul. 2013
Fig (Ficus Carica L.) Identification Based on Mutual Information and Neural Networks
Full Text (PDF, 432KB), PP.50-57
The process of recognition and identification of plant species is very time-consuming as it has been mainly carried out by botanists. The focus of computerized living plant's identification is on stable feature's extraction of plants. Leaf-based features are preferred over fruits, also the long period of its existence than fruits. In this preliminary study, we study and propose neural networks and Mutual information for identification of two, three Fig cultivars (Ficus Carica L.) in Syria region. The identification depends on image features of Fig tree leaves. A feature extractor is designed based on Mutual Information computation. The Neural Networks is used with two hidden layers and one output layer with 3 nodes that correspond to varieties (classes) of FIG leaves. The proposal technique is a tester on a database of 84 images leaves with 28 images for each variety (class). The result shows that our technique is promising, where the recognition rates 100%, and 92% for the training and testing respectively for the two cultivars with 100% and 90 for the three cultivars. The preliminary results obtained indicated the technical feasibility of the proposed method, which will be applied for more than 80 varieties existent in Syria.
Cite This Paper
Ghada Kattmah,Gamil Abdel Azim,"Fig (Ficus Carica L.) Identification Based on Mutual Information and Neural Networks", IJIGSP, vol.5, no.9, pp.50-57, 2013.DOI: 10.5815/ijigsp.2013.09.08
M. Kumar, M. Kamble, S. Pawar, P. Patil and N. Bonde, "Survey on Techniques for Plant Leaf Classification", International Journal of Modern Engineering Research, vol. 1, no. 2, (2011), pp. 538-544.
Z. Zulkifli, "Plant Leaf Identification using Moment Invariants & General Regression Neural Network", Master Thesis, Universiti Teknologi Malaysia, (2009).
G. Wu, F. S. Bao, E. Y. Xu, Y. X. Wang, Y. F. Chang and Q. L. Xiang, "A Leaf Recognition Algorithm for Plant Classification using Probabilistic Neural Network", IEEE 7th International Symposium on Signal Processing and Information Technology, (2007).
K. Singh, I. Gupta and 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, no. 4, (2010), pp. 67-78.
M. Shabanzade, M. Zahedi and S. A. Aghvami, "Combination of Local Descriptors and Global Features for Leaf Classification", Signal Image Processing: An International Journal, vol. 2, no. 3, (2011), pp. 23-31.
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.
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.
D. Zhang, "Image Retrieval Based on Shape", Unpublished Dissertation, Monash University, 2002.
Q. K. Man, C. H. Zheng, X. F. Wang and F. Y. Lin, "Recognition of Plant Leaves using Support Vector", International Conference on Intelligent Computing, (2008), pp. 192-199, Shanghai.
A. Kadir, L.E. Nugroho, A. Susanto and P. I. Santosa, "Leaf Classification using Shape, Color, and Texture Features", International Journal of Computer Trends and Technology, vol. 1, no. 3, (2011), pp. 225-230.
A. Kadir, L.E. Nugroho, A. Susanto and P. I. Santosa, "Neural Network Application on Foliage Plant Identification", International Journal of Computer Applications, vol. 29, no. 9, (2011), pp. 15-22.
T.M. Cover, J.A. Thomas, Elements of Information Theory, Wiley, New Jersey, 2005.
A.J. Butte, I.S. Kohane, Mutual information relevance networks: functional genomic clustering using pair wise entropy measurements, PSB 5 (2000) 415–426.
G.S. Michaels, D.B. Carr, M. Askenazi, S. Fuhrman, X. Wen, R. Somogyi, Cluster analysis and data visualization of large scale gene expression data, PSB 3 (1998) 42–53.
Herzel H, Grosse I: Measuring correlations in symbols sequences. Physica A 1995, 216:518-542.
Steuer R, Kurths J, Daub CO, Weise J, Selbig J: The mutual information: Detecting and evaluating dependencies between variables.
B. Kröse, P. Van Der Smagt, An introduction to neural networks (8th ed.), 1996, available on http://www.fwi.uva.nl/research/neuro.
C. Bishop, Neural Networks for Pattern Recognition, Clarendon Press, Oxford, UK, 1995.
J.A. Freeman, D.M. Skapura, Neural Networks: Algorithms, Applications and Programming Techniques, Addison-Wesley, Reading, MA, 1991.
D.E. Rumelheart, G.E. Hinton, R.J. Williams, in: D.E. Rumelheart, J.L. McClelland (Eds.), Parallel Distributed Processing. Explorations in the Microstructure of Cognition, MIT Press, Cambridge, 1986, pp. 318–362.
D.E. Rumelheart, R. Durbin, R. Golden, Y. Chauvin, in: Y. Chauvin, D.E. Rumelheart (Eds.), Backpropagation: Theory, Architectures and Applications, Lawrence Erlbaum, Hillsdale, 1995, pp. 1–34.
D.E. Rumelheart, G.E. Hinton, R.J. Williams, Nature 323 (1986) 533.
Patterson, D. (1996). Artificial Neural Networks. Singapore: Prentice Hall.
Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. New York: Macmillan Publishing.
G A Azim and M.K. Sousow " Multi Layer Feed Forward Neural Networks For Olive trees Identification" IASTED. Conference on Artificial Intelligence and Application , 11-13 February. (AIA 2008) pp 420-426 Austria.