RGB - Based Color Texture Image Classification Using Anisotropic Diffusion and LDBP

In this paper, a novel color texture image classification based on RGB color space using anisotropic diffusion, and local directional binary patterns (LDBP) is introduced. Traditionally, RGB color space is widely used in digital images and hardware. RGB color space is applied to obtain more accurate color statistics for extracting features. According to characteristic of anisotropic diffusion, image is decomposed into cartoon approximation; further the texture approximation is obtained by subtracting the original image and cartoon approximation. Then, texture features of image are obtained by applying LDBP co-occurrence matrix parameters on texture approximation. LDA is used to enhance the class seperability. After feature extraction, k-NN classifier is used to classify texture classes by the extracted features. The proposed method is evaluated on Oulu database. Experimental results demonstrate the proposed method is better and more correct than RGB based color texture image classification methods in the literature.

[1]  Ramakant Nevatia,et al.  Structural Analysis of Natural Textures , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  John Daugman,et al.  Gabor wavelets for statistical pattern recognition , 1998 .

[3]  Rohini A. Bhusnurmath,et al.  A Novel Approach to Texture Classification using NSCT and LDBP , 2014 .

[4]  Robert Azencott,et al.  Texture Classification Using Windowed Fourier Filters , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  P. S. Hiremath And Rohini A. Bhusnurmath.  Nonsubsampled Contourlet Transform and Local Directional Binary Patterns for Texture Image Classification Using Support Vector Machine , 2013 .

[6]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[7]  A. Şengur.  COLOR TEXTURE CLASSIFICATION USING WAVELET TRANSFORM AND NEURAL NETWORK ENSEMBLES , 2009 .

[8]  Anil K. Jain,et al.  Texture Analysis , 2018, Handbook of Image Processing and Computer Vision.

[9]  Tieniu Tan,et al.  Brief review of invariant texture analysis methods , 2002, Pattern Recognit..

[10]  David G. Stork,et al.  Pattern Classification , 1973 .

[11]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[12]  Mark S. Nixon,et al.  Statistical geometrical features for texture classification , 1995, Pattern Recognit..

[13]  Rohini A. Bhusnurmath.  Texture Image Classification Using Nonsubsampled Contourlet Transform and Local Directional Binary Patterns , 2013 .

[14]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

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

[16]  Arif Ghafoor.  Multimedia database management systems , 1995, CSUR.

[17]  Maria Petrou,et al.  Image processing - dealing with texture , 2020 .

[18]  B Julesz,et al.  Experiments in the visual perception of texture. , 1975, Scientific American.

[19]  Arivazhagan Selvaraj,et al.  Texture classification using wavelet transform , 2003, Pattern Recognit. Lett..

[20]  Ming-Huwi Horng,et al.  Texture feature coding method for texture classification , 2003 .

[21]  A. Şengur.  Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification , 2008 .

[22]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Chiman Kwan,et al.  Target detection with texture feature coding method and support vector machines , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[24]  Engin Avci,et al.  Comparison of wavelet-SVM and wavelet-adaptive network based fuzzy inference system for texture classification , 2008, Digit. Signal Process..

[25]  P S Hiremath,et al.  PCA plus LDA on Wavelet Co-occurrence Histogram Features for Texture Classification and its Applications , 2013 .

[26]  James M. Keller,et al.  Texture description and segmentation through fractal geometry , 1989, Comput. Vis. Graph. Image Process..

[27]  Leslie M. Collins,et al.  Texture Features for Antitank Landmine Detection Using Ground Penetrating Radar , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Xianghua Xie,et al.  Handbook of Texture Analysis , 2008 .

[29]  R.M. Haralick,et al.  Statistical and structural approaches to texture , 1979, Proceedings of the IEEE.

[30]  Shyr-Shen Yu,et al.  HSV-based Color Texture Image Classification using Wavelet Transform and Motif Patterns , 2010 .

[31]  Srinivasan Ramakrishnan,et al.  SVD-Based Modeling for Image Texture Classification Using Wavelet Transformation , 2007, IEEE Transactions on Image Processing.

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

[33]  J. E. W. MAYHEW,et al.  Texture discrimination and Fourier analysis in human vision , 1978, Nature.

[34]  Matheel E. Abdulmunim.  Color Texture Classification Using Adaptive Discrete Multiwavelets Transform , 2012 .