Contourlet Transform Based Texture Analysis for Smoke and Fog Classification

Abstract:

Fire detection has long been an important research topic in image processing and pattern recognition, while smoke is a vital indication of fire’s existence. However, current smoke detection algorithms are far from meeting the requirements of practical applications. One major reason is that the existing methods can not distinguish smoke from fog because their colors and shapes are both very similar. This paper proposes a novel texture analysis based algorithm which has the ability to classify smoke and fog more efficiently. First the texture images are decomposed using Contourlet Transform (CT), and then we extract the feature vector from Contourlet coefficients, finally we make use of Support Vector Machine (SVM) to classify the textures. Experiments are performed on the sample images of smoke and fog taking accuracy rate of classification as evaluation criterion, and the accuracy rate of our algorithm is 97%. To illustrate its performance, our method has also been compared with the algorithms using Gray Level Co-occurrence Matrixes (GLCM), Local Binary Pattern (LBP) and Wavelet Transform (WT).

• Periodical: Applied Mechanics and Materials (Volumes 88-89)
• Pages: 537-542
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.88-89.537
• Online since: August 2011
• Authors: Yang Zhao, Jian Hui Zhao, Jing Huang, Shi Zhong Han, Cheng Jiang Long, Zhi Yong Yuan, Deng Yi Zhang
• Keywords: Contourlet Transform, Kernel Function, Pattern Classification, Support Vector Machine (SVM), Texture Analysis

References

[1] Y. Cui, H. Dong, E.Z. Zhou, An Early Fire Detection Method Based on Smoke Texture Analysis and Discrimination, Proceedings of 2008 Congress on Image and Signal Processing, Vol. 3, pp.95-99, (2008).

DOI: 10.1109/cisp.2008.397

[2] B.C. Ko, K.H. Cheong, J.Y. Nam, Fire detection based on vision sensor and support vector machines, Fire Safety Journal, Vol. 44, pp.322-329, (2009).

DOI: 10.1016/j.firesaf.2008.07.006

[3] L.K. Soh, C. Tsatsoulis, Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices, IEEE Transaction on Geoscience and Remote Sensing, Vol. 37, pp.780-795, (2002).

DOI: 10.1109/36.752194

[4] M. Unser, Texture classification and segmentation using wavelet frames, IEEE Transactions on Image Processing, Vol. 4, pp.1549-1560, (2002).

DOI: 10.1109/83.469936

[5] P.P. Ohanian, R.C. Dubes, Performance evaluation for four classes of texture feature, Pattern Recognition, Vol. 25, pp.819-833, (1992).

DOI: 10.1016/0031-3203(92)90036-i

[6] T.S. Lee, Image representation using 2D Gabor wavelets, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, pp.837-842, (1996).

DOI: 10.1109/34.541406

[7] M.X. Liu, P. Xu, X.H. Xun, Texture classification and recognition based on gabor filter, Computer Development and Appliccations, Vol. 4, pp.2-3, (2008).

[8] T. Ojala, M. Pietikainen, T. Maenpaa, Multi-resolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 24, pp.971-987, (2002).

DOI: 10.1109/tpami.2002.1017623

[9] J. Huang, J.H. Zhao, W.W. Gao, Local binary pattern based texture analysis for visual fire recognition, Proceedings of the 3rd International Congress on Image and Signal Processing, Vol. 4, pp.1887-1991, (2010).

DOI: 10.1109/cisp.2010.5647609

[10] M.N. Do, M. Vetterli, The Contourlet Transform: an efficient directional multiresolution image representation, IEEE Transactions on Image Processing, Vol. 14, pp.2091-2106, (2005).

DOI: 10.1109/tip.2005.859376

[11] D. Duncan, M.N. Do, Directional multiscale modeling of images using Contourlet Transform, IEEE Transactions on Image Processing, Vol. 15, pp.1-10, (2006).

DOI: 10.1109/tip.2006.873450

[12] T.T. Nguyen, H. Chauris, Uniform discrete Curvelet Transform, IEEE Transactions on Signal Processing, Vol. 58, pp.3618-3634, (2010).

DOI: 10.1109/tsp.2010.2047666

[13] R.H. Bamberger, M.J.T. Smith, A filter banks for the directional decomposition of images: theory and design,. IEEE Transactions on Signal Processing, Vol. 40, pp.882-893, (1992).

DOI: 10.1109/78.127960

[14] T.T. Nguyen, S. Oraintara, A class of Multiresolution Directional Filter Banks, IEEE Transactions on Signal Processing, Vol. 55, pp.949-960, (2007).

DOI: 10.1109/tsp.2006.887140

[15] Y.F. Zhao, L.Z. Xia, Contourlet-based feature extraction for texture images, Signal Processing, Vol. 26, pp.161-165, (2010).