Texture Classification System Based on 2D-DOST Feature Extraction Method and LS-SVM Classifier

In this paper, a new 2D-DOST (Two-Dimensional Discrete Orthonormal Stockwell Transform) and LS-SVM (Least Squares Support Vector Machines) based classifier system is proposed for classification of texture images. The proposed system contains two main stages. These stages are feature extraction and classification. In the feature extraction stage, the distinguishing feature vectors which represent descriptive features of texture images are obtained by using a 2D-DOST based feature extraction method. In the classification stage, the texture images are classified by the LS-SVM since this classifier has high success rate and accuracy. The training of LS-SVM is performed on the distinguishing feature vector of each texture component. Texture samples are recognized by the test data applied to the input of trained LS-SVM classifier. Performance evaluations of the proposed method are carried on different datasets obtained from sub-images. These datasets include both the normal texture images and noise added images. Sub-images into datasets are derived from Brodatz and Kylberg texture images database. Gaussian and Salt & Pepper noise with different levels are used for creating noisy datasets. According to the study results, the proposed 2D-DOST and LS-SVM based classifier has a capability of classifying texture images with high success rate and noise robustness.

[1]  Rui Wang,et al.  Sub Oriented Histograms of Local Binary Patterns for Smoke Detection and Texture Classification , 2016, KSII Trans. Internet Inf. Syst..

[2]  Matti Pietikäinen,et al.  Median Robust Extended Local Binary Pattern for Texture Classification , 2016, IEEE Transactions on Image Processing.

[3]  B. Prasad,et al.  Lattice vector quantisation for indexing and retrieval of medical images using texture features based on 2-D Wold decomposition , 2015 .

[4]  Romi Satria Wahono,et al.  Color and Texture Feature Extraction Using Gabor Filter - Local Binary Patterns for Image Segmentation with Fuzzy C-Means , 2015 .

[5]  Usman Qamar,et al.  Texture Classification Using Rotation- and Scale-Invariant Gabor Texture Features , 2013, IEEE Signal Processing Letters.

[6]  Jinwen Ma,et al.  Wavelet-Based Image Texture Classification Using Local Energy Histograms , 2011, IEEE Signal Processing Letters.

[7]  Tianyou Chai,et al.  Designing compact Gabor filter banks for efficient texture feature extraction , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[8]  Yanwei Wang,et al.  The discrete orthonormal Stockwell transform for image restoration , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[9]  Engin Avci,et al.  An optimum feature extraction method for texture classification , 2009, Expert Syst. Appl..

[10]  Yanwei Wang,et al.  On the use of the Stockwell transform for image compression , 2009, Electronic Imaging.

[11]  Turgay Çelik,et al.  Multiscale texture classification using dual-tree complex wavelet transform , 2009, Pattern Recognit. Lett..

[12]  Sylvia Drabycz,et al.  Image Texture Characterization Using the Discrete Orthonormal S-Transform , 2008, Journal of Digital Imaging.

[13]  Claude Cariou,et al.  Unsupervised texture segmentation/classification using 2-D autoregressive modeling and the stochastic expectation-maximization algorithm , 2008, Pattern Recognit. Lett..

[14]  Prabir Kumar Biswas,et al.  Texture image retrieval using rotated wavelet filters , 2007, Pattern Recognit. Lett..

[15]  Tae Jin Kang,et al.  Texture classification and segmentation using wavelet packet frame and Gaussian mixture model , 2007, Pattern Recognit..

[16]  Abdulkadir Sengür,et al.  Wavelet packet neural networks for texture classification , 2007, Expert Syst. Appl..

[17]  Shutao Li,et al.  Comparison and fusion of multiresolution features for texture classification , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[18]  Yaonan Wang,et al.  Texture classification using the support vector machines , 2003, Pattern Recognit..

[19]  Alessandro Neri,et al.  Model based rotation-invariant texture classification , 2002, Proceedings. International Conference on Image Processing.

[20]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Stan Z. Li,et al.  Markov Random Field Modeling in Computer Vision , 1995, Computer Science Workbench.

[22]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[23]  Jana Reinhard,et al.  Textures A Photographic Album For Artists And Designers , 2016 .

[24]  Sasan Mahmoodi,et al.  Rotation invariant texture descriptors based on Gaussian Markov random fields for classification , 2016, Pattern Recognit. Lett..

[25]  André Ricardo Backes,et al.  Texture analysis and classification: A complex network-based approach , 2013, Inf. Sci..

[26]  Mr. S. R. Suralkar,et al.  Texture Image Classification Using Support Vector Machine , 2012 .

[27]  Gustaf Kylberg,et al.  Kylberg Texture Dataset v. 1.0 , 2011 .

[28]  Abdulkadir Sengür,et al.  Wavelet domain association rules for efficient texture classification , 2011, Appl. Soft Comput..

[29]  Author manuscript, published in "35th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010), Dallas, TX: United States (2010)" QUATERNIONIC WAVELETS FOR TEXTURE CLASSIFICATION , 2010 .

[30]  Robert Glenn Stockwell,et al.  A basis for efficient representation of the S-transform , 2007, Digit. Signal Process..

[31]  Rangachar Kasturi,et al.  Machine vision , 1995 .