Abstract
In this paper we are trying to identify the best feature extraction method for classifying war scene from natural scene using Artificial Neural Networks. Also, we are proposed a new hybrid method for the same. For this purpose two set of image categories are taken viz., opencountry & war tank. By using the proposed hybrid method and other feature extraction methods like haar wavelet, daubechies (db4) wavelet, Zernike moments, Invariant moments, co-occurrence features & statistical moments, features are extracted from the images/scenes. The extracted features are trained and tested with Artificial Neural Networks (ANN) using feed forward back propagation algorithm. The comparative results are proving efficiency of the proposed hybrid feature extraction method (i.e., the combination of GLCM & Statistical moments) in war scene classification problems. 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gokalp, D., Aksoy, S.: Scene Classification Using Bag-of-Regions Representations. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1–8 (2007)
Vailaya, A., Figueiredo, A., Jain, A., Zhang, H.: Image classification for content-based indexing. IEEE Transactions on Image Processing 10, 117–129 (2001)
Bosch, A., Zisserman, A., Muñoz, X.: Scene classification using a hybrid enerative/discriminative approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 30(4), 712–727 (2008)
Chella, A., Frixione, M., Gaglio, S.: Understanding dynamic scenes. Artificial Intelligence 123, 89–132 (2000)
Szummer, M., Picard, R.W.: Indoor-Outdoor Image Classification. In: Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD 1998), January 03, p. 42 (1998)
Zhang, L., Li, M., Zhang, H.-J.: Boosting Image Orientation Detection with Indoor vs. Outdoor Classification. In: Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision, December 03-04, p. 95 (2002)
Bosch, A., Munoz, X., Freixenet, J.: Segmentation and description of natural outdoor scenes. Image and Vision computing 25, 727–740 (2007)
Payne, A., Singh, S.: Indoor vs outdoor scene classification in digital photographs. Pattern Recognition 38, 1533–1545 (2005)
Arivazhagan, S., Ganesan, L.: Texture Segmentation Using Wavelet Transform. Pattern Recognition Letters 24(16), 3197–3203 (2003)
Daniel Madan Raja, S., Shanmugam, A.: ANN and SVM Based War Scene Classification using Wavelet Features: A Comparative Study. Journal of Computational Information Systems 7(5), 1402–1411 (2011)
Daniel Madan Raja, S., Shanmugam, A.: Zernike Moments Based War Scene Classification using ANN and SVM: A Comparative Study. Journal of Information and Computational Science 8(2), 212–222 (2011)
Daniel Madan Raja, S., Shanmugam, A.: ANN and SVM based War Scene Classification using Invariant Moments and GLCM Features: A Comparative Study. In: Proceedings of 3rd International Conference on Machine Learning and Computing (ICMLC 2011), February 26-28, vol. 3, pp. 508–512 (2011)
Wang, J.Z., Wiederhold, G., Firschein, O., Wei, S.X.: Content-Based Image Indexing and Searching Using Daubechies’ Wavelets. Int. J. on Digital Libraries 1(4), 311–328 (1997)
Zernike, F.: Beugungstheorie des schneidenverfahrens und seiner verbesserten form, derphasenkontrastmethode. Physica 1, 689–704 (1934)
Khotanzad, A., Hong, Y.H.: Invariant Image Recognition by Zernike Moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5), 489–497 (1990)
Teh, C.H., Chin, R.T.: On image analysis by the methods of moments. IEEE Trans. Pattern Anal. Machine Intell. 10, 496–512 (1988)
Hu, M.K.: Visual pattern recognition by moments invariants. IRE Trans. Information Theory 8, 179–187 (1962)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6), 610–621 (1973)
McCulloch, W., Pitts, W.: A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)
Hebb, D.O.: The Organization of Behavior: A Neuropsychological Theory. Wiley, New York (1949)
Werbo, P.J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Harvard University (1974)
Rumelhart, D., Hinton, G., Williams, R.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raja, S.D.M., Shanmugam, A., Srinitya, G. (2011). Artificial Neural Networks Based War Scene Classification Using Various Feature Extraction Methods: A Comparative Study. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-23896-3_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23895-6
Online ISBN: 978-3-642-23896-3
eBook Packages: Computer ScienceComputer Science (R0)