Classifying Similarity and Defect Fabric Textures based on GLCM and Binary Pattern Schemes
Автор: R. Obula Konda Reddy, B. Eswara Reddy, E. Keshava Reddy
Журнал: International Journal of Information Engineering and Electronic Business(IJIEEB) @ijieeb
Статья в выпуске: 5 vol.5, 2013 года.
Бесплатный доступ
Textures are one of the basic features in visual searching,computational vision and also a general property of any surface having ambiguity. This paper presents a texture classification system which has high tolerance against illumination variation. A Gray Level Co-occurrence Matrix (GLCM) and binary pattern based automated similarity identification and defect detection model is presented. Different features are calculated from both GLCM and binary patterns (LBP, LLBP, and SLBP). Then a new rotation-invariant, scale invariant steerable decomposition filter is applied to filter the four orientation sub bands of the image. The experimental results are evaluated and a comparative analysis has been performed for the four different feature types. Finally the texture is classified by different classifiers (PNN, K-NN and SVM) and the classification performance of each classifier is compared. The experimental results have shown that the proposed method produces more accuracy and better classification accuracy over other methods.
Feature extraction, K-NN, LBP, LLBP, SLBP, Steerable filter decomposition, SVM and PNN
Короткий адрес: https://sciup.org/15013208
IDR: 15013208
Список литературы Classifying Similarity and Defect Fabric Textures based on GLCM and Binary Pattern Schemes
- Dattatraya, S.Bormane, Shailendrakumar, M. Mukane,Sachin and R. Gengaje, "Rotation Invariant Texture Classification using Fuzzy Logic”, International Journal of Computer Application, vol. 41,no. 1,pp. 41-44, 2012.
- R.S.Sabeenian, M.E.Paramasivam and P.M.Dinesh, "Fabric defect detection in handlooms cottage silk industries using image processing Techniques", International Journal of Computer Applications, vol. 58, no. 11, pp. 21-29, 2012.
- A. Suresh and K. L. Shunmuganathan, ”Image Texture Classification using Gray Level Co-Occurrence Matrix Based Statistical Features”, European Journal of Scientific Research, vol.75, no.4 , pp. 591-597, 2012.
- C.Callins Christiyana and V.Rajaman, "Comparison of Local Binary Pattern Variants for Ultrasound Kidney Image Retrieval", International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, Iss. 10, pp. 224-228, October 2012.
- Alceu Ferraz Costa, Gabriel Humpire-Mamani and Agma Juci Machado Traina,"An Efficient Algorithm for Fractal Analysis of Textures", Graphics, Patterns and Images (SIBGRAPI) Conference, pp.39-46, 2012.
- Manoj Kumar, Sagun Kumar sudhansu and Kasabegoudar, "Wavelet Based Texture Analysis and Classification with Linear Regration Model", International Journal of Engineering Research and Applications, vol. 2, Iss. 5, pp. 1963-1970, 2012.
- Hamid Reza Eghtesad Doost, "An Efficient Method for Texture Classification with Local Binary Pattern Based on Wavelet Transformation", International Journal of Engineering Science and Technology, vol. 4, No. 12,pp. 4881-4885,2012.
- E.M.Srinivasan, K.Ramar and A.Suruliandi, "Rotation Invariant Texture Classification using Fuzzy Local Texture Patterns", International Journal of Computer Science and Technology, vol. 3, Iss. 1, 2012.
- Mahesh1 and M.V.Subramanyam, "Invariant Corner Detection Using Steerable Filters and Harris Algorithm", An International Journal on Signal & Image Processing, vol. 3, No. 5, pp. 111-118, 2012.
- Abdul Kadir, Lukito Edi Nugroho, Adhi Susanto and Paulus Insap Santosa,"Leaf Classification Using Shape, Color, and Texture Features”, International Journal of Computer Trends and Technology, pp. 225-230, 2011.
- R.S.Sabeenian and V.Palanisamy, "Texture Image Classification using Multi Resolution Combined Statistical and Spatial Frequency Method", International Journal of Technology And Engineering System(IJTES), Vol. 2, No. 2, pp. 167-171, 2011.
- Xiang-Yang Wang, Yong-Jian Yu, Hong-Ying Yang, “An effective image retrieval scheme using color, texture and shape features”, Computer standard and interfaces, pp. 59-68,2010.
- Barron, Jonathan Malik and Jitendra, "Discovering Efficiency in Coarse-To-Fine Texture Classification", June 12, 2010.
- R.Suguna and P.Anandhakumar, "A Rotation Invariant Pattern Operator for Texture Characterization", International Journal of Computer Science and Network Security, vol. 10, No. 4, pp. 120-129, 2010.
- S. Liao, Max W. K. Law and Albert C. S. Chung, “Dominant Local Binary Patterns for Texture Classification”, IEEE Transactions on Image Processing, vol. 18, no. 5, pp. 1107-1118, May 2009.
- B.V. Ramana Reddy, A. Suresh, M. Radhika Mani and V.Vijaya Kumar, ”Classification of Textures Based on Features Extracted from Preprocessing Images on Random Windows”, International Journal of Advanced Science and Technology, vol. 9, pp. 9-18, 2009.
- Jing Yi Tou, Yong Haur Tay and Phooi Yee Lau, “Recent trends in texture classification: a review”, Symposium on Progress in Information & Communication Technology, pp. 63-68, 2009.
- Matteo Masotti and Renato Campanini, "Texture classification using invariant ranklet features”, Pattern Recognition Letters, vol. 29, Iss. 14, pp. 1980–1986, 15, October 2008.
- Alireza Tavakoli Targhi, Jan-mark Geusebroek and Andrew Zisserman, "Texture Classification with Minimal Training Images", IEEE International Conference on Pattern Recognition, 2008.
- Di Huang, Caifeng Shan, Mohsen Ardebilian and Liming Chen,"Facial Image Analysis Based on Local Binary Patterns: A Survey", pp. 1-14, 2008.
