Artificial Neural Network-Based Texture Classification Using Reduced Multidirectional Gabor Features

In this paper, a technique to classify Engineering Machined Textures (EMT) into the six classes of Turning, Grinding, Horizontal-Milling, Vertical-Milling, Lapping and Shaping, is presented. Multidirectional Gabor features are firstly extracted from each image followed by a dimensionality reduction step using Principal Components Analysis (PCA). The images are finally classified using a supervised Artificial Neural Network (ANN) classifier. Experimental results using a 72-image dataset demonstrate that PCA is able to reduce computational time while improving classification accuracy. In addition, the use of the proposed Gabor filter seems to be more robust compared to other existing techniques.

[1]  Graeme G. Wilkinson,et al.  Results and implications of a study of fifteen years of satellite image classification experiments , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Youssef Chahir,et al.  Spectral clustering and dimensionality reduction applied to Content based image retrieval with hybrid Descriptors , 2009 .

[3]  Darío Baptista,et al.  A survey of artificial neural network training tools , 2013, Neural Computing and Applications.

[4]  M. R. Turner,et al.  Texture discrimination by Gabor functions , 1986, Biological Cybernetics.

[5]  M. A. Younes,et al.  Online quality monitoring of perforated steel strips using an automated visual inspection (AVI) system , 2011, 2011 IEEE International Conference on Quality and Reliability.

[6]  Fatimah Khalid,et al.  SUPERVISED ANN CLASSIFICATION FOR ENGINEERING MACHINED TEXTURES BASED ON ENHANCED FEATURES EXTRACTION AND REDUCTION SCHEME , 2013 .

[7]  J. Kruskal.  Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis , 1964 .

[8]  J. Paulo Davim,et al.  Surface Texture Characterization and Evaluation Related to Machining , 2010 .

[9]  Khaled M. Mahar,et al.  Supervised texture classification using several features extraction techniques based on ANN and SVM , 2008, 2008 IEEE/ACS International Conference on Computer Systems and Applications.

[10]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[11]  TagliaferriRoberto,et al.  Textural identification of carbonate rocks by image processing and neural network , 2005 .

[12]  Jing Zhang,et al.  Feature Reduction and Texture Classification in MRI-Texture Analysis of Multiple Sclerosis , 2007, 2007 IEEE/ICME International Conference on Complex Medical Engineering.

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

[14]  Ronald G. Harley,et al.  Identification and control of induction machines using artificial neural networks , 1993 .

[15]  Wilson S. Geisler,et al.  Texture segmentation using Gabor modulation/demodulation , 1987, Pattern Recognit. Lett..

[16]  Richa Singh,et al.  An analysis of the performance of Artificial Neural Network technique for apple classification , 2012, AI & SOCIETY.

[17]  A. Gebejes,et al.  Texture Characterization based on Grey-Level Co-occurrence Matrix , 2013 .

[18]  A. Kassner,et al.  Texture Analysis: A Review of Neurologic MR Imaging Applications , 2010, American Journal of Neuroradiology.

[19]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

[20]  Siddhivinayak Kulkarni,et al.  Texture Feature Extraction and Classification by Combining Statistical and Neural Based Technique for Efficient CBIR , 2012, FGIT-MulGraB/BSBT/IUrC.

[21]  Michal Strzelecki,et al.  Texture Analysis Methods - A Review , 1998 .

[22]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[23]  Doaa Mahmoud-Ghoneim,et al.  The impact of image dynamic range on texture classification of brain white matter , 2008, BMC Medical Imaging.

[24]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[25]  Robert P. W. Duin,et al.  Dimensionality reduction of image features using the canonical contextual correlation projection , 2005, Pattern Recognit..

[26]  Kishan G. Mehrotra,et al.  Elements of artificial neural networks , 1996 .