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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(6): 869-873 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.255

ANN and SVM Based War Scene Classification Using Invariant Moments and GLCM Features: A Comparative Study

S. Daniel Madan Raja and A. Shanmugam
Abstract—Scene classification underlies many problems in visual perception such as object recognition and environment navigation. In this paper we are trying to classify a war scene from the natural scene. For this purpose two set of image categories are taken viz., opencountry & war tank. By using Invariant Moments and Gray Level Co-occurrence Matrix (GLCM), features are extracted from the images. The extracted features are trained and tested with (i) Artificial Neural Networks (ANN) using feed forward back propagation algorithm and (ii) Support Vector Machines (SVM) using radial basis kernel function with p=5. The comparative results are proving efficiency of Support Vector Machines towards war scene classification problems by using GLCM feature extraction method. Although this study has been the first step of the research in the area of scene classification, the results have shown that the war scenes can be successfully classified from opencountry. It can be concluded that the proposed work significantly and directly contributes to scene classification and its new applications. The complete work is experimented in Matlab 7.6.0 using real world dataset.

Index Terms—Invariant moments, gray level Co-Occurrence matrix, scene classification, artificial neural networks, support vector machine.

Daniel Madan Raja S is with the Department of Information Technology, Bannari Amman Institute of Technology, Tamil Nadu, India (e-mail: cross4u@rediff.com).
Shanmugam A is with the Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Tamil Nadu, India (e-mail: dras_bit@yahoo.com).

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Cite:S. Daniel Madan Raja and A. Shanmugam, "ANN and SVM Based War Scene Classification Using Invariant Moments and GLCM Features: A Comparative Study," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 869-873, 2012.

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