TEXTURE IMAGE CLASSIFICATION BY STATISTICAL FEATURES OF WAVELET
DOI:
https://doi.org/10.29284/ijasis.5.1.2019.1-7Keywords:
Texture classification, discrete wavelet transform, k-nearest neighbor, classification, Brodatz databaseAbstract
An unidentified image sample is assigned to a recognized texture class is known as Texture Classification (TC). The main challenging task in TC is the non uniformity changes in orientation, visual appearance and scale. Texture is an important feature in computer analysis for the purpose of classification. In this paper, an efficient TC system based on Discrete Wavelet Transform (DWT) is presented. The performance of the system is evaluated by Brodatz database. At first, the DWT is used to decompose the input texture image for feature extraction at a particular decomposition level. From each sub-band coefficients statistical features are extracted. Finally, k-Nearest Neighbour (kNN) classifier is used for classification. Results show that a better classification accuracy of 94.72% is achieved by the features of 3rd level DWT and kNN classifier.
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References
M. Jian, L. Liu, and F. Guo, “Texture image classification using perceptual texture features and Gabor wavelet features”, Asia-Pacific Conference on Information Processing, Vol. 2, 2009, pp. 55-58.
Y.B. Salem, and S. Nasri, “Rotation invariant texture classification using support vector machines”, International Conference on Communications, Computing and Control Applications, 2011, pp. 1-6.
Y. Shang, Y.H. Diao, and C.M. Li, “Rotation invariant texture classification algorithm based on curvelet transform and SVM”, International Conference on Machine Learning and Cybernetics, Vol. 5, 2008, pp. 3032-3036.
Y. Qiao, Y. Zhao, and X. Song, “Dynamic Texture Classification Based on Motion Statistical Feature Matrix”, International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2013, pp. 535-538.
Z. Xiangbin, “Texture classification based on contourlet and support vector machines”, International Colloquium on Computing, Communication, Control, and Management, Vol. 2, 2009, pp. 521-524.
Q. Wang, H. Li, and J. Liu, “Subset selection using rough set in wavelet packet based texture classification”, International Conference on Wavelet Analysis and Pattern Recognition, Vol. 2, 2008, pp. 662-666.
A. Porebski, N. Vandenbroucke, and L. Macaire, “Iterative feature selection for color texture classification”, IEEE International Conference on Image Processing, Vol. 3, 2007, pp. III-509.
A. Hafiane, and B. Zavidovique, “Local relational string for textures classification”, International Conference on Image Processing, 2006, pp. 2157-2160.
A. Jayasudha, and D. Pugazhenthi, “Classification of Textures using Patch Based Energy Features of Selected Wavelet Coefficients”, Journal of Applied Sciences, Vol. 14, No. 7, 2014, pp. 709-713.
Q. Yu-long, and W. Fu-shan, “Dynamic texture classification based on dual-tree complex wavelet transform”, International Conference on Instrumentation, Measurement, Computer, Communication and Control, 2011, pp. 823-826.
M.C. Lee, and C.M. Pun, “Texture classification using dominant wavelet packet energy features”, IEEE Southwest Symposium on Image Analysis and Interpretation, 2000, pp. 301-304.
S. Pattnaik, M. Dash, and S.K. Sabut, “DWT-based feature extraction and classification for motor imaginary EEG signals”, International Conference on Systems in Medicine and Biology, 2016, pp. 186-201.
A. Senol, K. Dınçer, H. Sever, and E. Elbaşi, “Blocked-DWT based vector image watermarking”, Signal Processing and Communications Applications Conference, 2015, pp. 264-267.
S. Murugan, and C. Srinivasan, “Underwater Object Recognition Using KNN Classifier”, International Journal of MC Square Scientific Research Vol.9, No.3, 2017, pp. 48-52.
M. Suresha, K.N. Shreekanth, and B.V. Thirumalesh, “Recognition of diseases in paddy leaves using knn classifier”, International Conference for Convergence in Technology, 2017, pp. 663-666.
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