Open Access Open Access  Restricted Access Subscription or Fee Access

Hybrid Classification Models using ANN and Fuzzy Support Vector Machines on Spatial Databases

D. N. Vasundhara, Dr. M. Seetha

Abstract


Image classification is one of classical problem for many aspects of remote sensing in which to extract some of the important spatially variable parameters, global change studies and environmental applications. In the literature, various classification methods have been developed for classification of images such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Fuzzy Support Vector Machines (FSVM), Genetic algorithms (GA) and Decision Trees (DT). In this paper, we propose a combined scheme for spatial image classification, which is composed of FSVM and ANN techniques. The proposed classification scheme consists of two main steps: Firstly, we separate the each image into class by an ANN classifier based on the features of images and in the second step, the FSVM classifier has been applied on the output of ANN.  This can be denoted as ANN_FSVM classifier. A comparison of techniques for spatial data has been given in this paper.

Keywords


Artificial Neural Network, Feature Extraction, Fuzzy Support Vector Machine, Image Classification.

Full Text:

PDF

References


M. Seetha, I.V.Muralikrishna, B.L.Deekshatulu, B.L.Malleswari, Nagaratna, P.Hedge, Artificial Neural Networks And Other Methods of Image Classification”, Journal of Theoretical & Applied Information Technology 4.11 (2008)

D. Lu, Q. Weng, A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, 2007,Vol. 28, No. 5, pp.823-870.

Wang, Haihui, et al. Classification of remote sensing agricultural image by using artificial neural network." Intelligent Systems and Applications,. ISA 2009.International Workshop on. IEEE, 2009.

Ahmed, Shohel Ali, SnigdhaDey, and Kandarpa Kumar Sarma. Image Texture Classification Using Artificial Neural Network (ANN)." Emerging Trends and Applications in Computer Science (NCETACS), 2011 2nd National Conference on IEEE, 2011.

Mahmon, NurAnis, and Norsuzila Ya'acob. A review on classification of satellite image using Artificial Neural Network (ANN)." Control and System Graduate Research Colloquium (ICSGRC), 2014 IEEE 5th. IEEE, 2014.

AyoubAra, Youssef Sa, RkiaFajr and Abdelaziz Bouroumi, Classification of Mammographic Images using Artificial Neural Network", Applied Mathematical Sciences, Vol. 7, no. 89, 4415 - 4423, 2013

Hai, Tran Son, and Nguyen Thanh Thuy. Image Classification using Support Vector Machine and Artificial Neural Network." International Journal of Information Technology and Computer Science (IJITCS) 4.5 (2012): 32.

Lin, Chun-Fu, and Sheng-De Wang. Fuzzy support vector machines." Neural Networks, IEEE Transactions on 13.2 (2002): 464-471.

Inoue, Takuya, and Shigeo Abe. Fuzzy support vector machines for pattern classification." Neural Networks, 2001. Proceedings. IJCNN'01. International Joint Conference on. Vol. 2. IEEE, 2001.

Abe, Shigeo, and Takuya Inoue. Fuzzy support vector machines for multiclass problems." ESANN. 2002.

Li,Jianming, et al. Image classification based on fuzzy support vector machine."Computational Intelligence and Design, 2008. ISCID'08. International Symposiumon. IEEE, Vol. 1. , 2008.

Chen, Degang, Qiang He, and Xizhao Wang. FRSVMs: Fuzzy rough set basedsupport vector machines." Fuzzy Sets and Systems 161.4 (2010): 596-607.

Cervantes, Jair, Xiaoou Li, and Wen Yu. Support vector machine classification based on fuzzy clustering for large data sets." MICAI 2006: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2006. 572-582.

Chen, Xiujuan, et al. Combining SVM classifiers using genetic fuzzy systems based on auc for gene expression data analysis." Bioinformatics Research and Applications. Springer Berlin Heidelberg, 2007. 496-505.

Hao Jiang, Wai-Ki Ching,Zeyu Zheng,"Kernel Techniques in Support Vector Machines for Classification of Biological Data", IJITCS, 2011, Vol.3, No.2, pp.1-8.

Le Hoang Thai, Tran Son Hai and Nguyen ThanhThuy, “Image Classification using Support Vector Machine and Artificial Neural Network”, I.J. Information Technology and Computer Science, 5, 32-38, 2012.

A. Shilton, D.T.H. Lai, Iterative fuzzy support vector machine classification. IEEE International Fuzzy Systems Conference (2007), pp. 1–6.

V. N. Vapnik, Statistical Learning Theory, New York: John Wiley & Sons, 1998.

Durgesh k. Srivastava, lekha bhambhu, data classification using support vector machine,” Journal of Theoretical & Applied Information Technology 4.11 (2009).

Bishop, C.: Pattern Recognition and Machine Learning. Springer Press, 2006.

S Haykin, Neural Network - a Comprehensive Foundation; a Computational Approach to Learning and Machine Intelligence, Macmillan, NY, 1994.

Pooja Kamavisdar, Sonam Saluja, Sonu Agrawal, A Survey on Image Classification Approaches and Techniques, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 1, January 2013.

Sahiner, Berkman et.al. Image Classification using Artificial Neural Networks, Ann Arbor 1001 (1995): 48109-0030.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.