Developing a New Version of Local Binary Patterns for Texture Classification
Subject Areas : electrical and computer engineeringM. Pakdel 1 , M. H. Shakoor 2 *
1 -
2 -
Abstract :
Texture classification is one of the main steps in image processing and computer vision applications. Feature extraction is the first step of texture classification process which plays a main role. Many approaches have proposed to classify textures since now. Among them, Local Binary Patterns and Modified Local Binary Patterns, because of simplicity and classification accuracy, have emerged as one of the most popular ones. The Local Binary Patterns have simple implementation, but with increase in the radius of neighborhood, computational complexity will be increased. Modified Local Binary Patterns assigns various labels to uniform textures and a unique label to all non-uniform ones. In this respect, the modified local binary pattern can't classify non uniform textures as well as uniform ones. In this paper a new version of Local Binary Pattern is proposed that has less computational complexity than Local Binary Patterns and more classification accuracy than Modified version. The proposed approach classifies non uniform textures as well as uniform ones. Also with change in the length of central gray level intervals, locality and globally of the features can be controlled. Classification accuracy on two standard datasets, Brodatz and Outex, indicates the efficiency of the proposed approach.
[1] M. Petrou and P. G. Sevilla, Image Processing Dealing with Texture, Ch. 2, John Wiely and Sons Ltd, pp. 1-6, 2006.
[2] M. Tuceryan and A. K. Jain, "Texture Analysis," The Handbook of Pattern Recognition and Computer Vision, Ch. 2, pp. 207-248, World Scientific Publishing Co., 1998.
[3] R. M. Haralick, K. Shanmugam, and I. Dinestein, "Textural features for image classification," IEEE Trans. on System, Man and Cybernetic, vol. 3, no. 6, pp. 610-621, Nov. 1979.
[4] D. Popescu, R. Dobrescu, and M. Nicolae, "Texture classification and defect detection by statistical features," Int. J. of Circuit, System, and Signal Processing, vol. 1, no. 1, pp. 79-85, 2007.
[5] G. R. Cross and A. K. Jain, "Markov random field texture models," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 5, no. 1, pp. 25-39, Jan. 1983.
[6] M. Unser, "Texture classification and segmentation using wavelet frames," IEEE Trans. on Image Processing, vol. 4, no. 11, pp. 1549-1560, Nov. 1995.
[7] F. Bianconi and A. Fernandez, "Evaluation of the effects of Gabor filter parameters on texture classification," Pattern Recognition, vol. 40, no. 12, pp. 3325-3335, 2007.
[8] M. Pakdel and F. Tajeripour, "Texture classification using optimal Gabor filters," in Proc. Int. eConf. on Computer and Knowledge Engineering, pp. 208-213, 13-14 Oct. 2011.
[9] X. Chen, X. Zeng, and D. van Alphen, "Multi-class feature selection for texture classification," Pattern Recognition Letters, vol. 27, no. 14, pp. 1685-1691, 2006.
[10] M. Pietikainen, T. Ojala, and Z. Xu, "Rotation-invariant texture classification using feature distributions," Pattern Recognition, vol. 33, pp. 43-52, 2000.
[11] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, Jul. 2002.
[12] S. Arivazhagan and L. Ganesan, "Texture classification using wavelet transform," Pattern Recognition Letters, vol. 24, no. 9-10, pp. 1513-1521, 1 Jun. 2003.
[13] B. V. Ramana Reddy, M. Radhika Mani, and K. V. Subbaiah, "Texture classification method using wavelet transform based on Gaussian Markov random field," International J. of Signal and Image Processing, vol. 1, no. 1, pp. 35-39, 2010.
[14] V. Vijaya Kumar, U. S. N. Raju, M. Radhika Mani, and A. L. Narasimha Rao, "Wavelet based texture segmentation methods based on combinatorial of morphological and statistical operations," International J. of Computer Science and Network Security, vol. 8, no. 8, pp. 176-181, Aug. 2008.
[15] J. S. Weszka, C. R. Dyer, and A. Rosenfeld, "A comparative study of texture measures for terrain classification," IEEE Trans. on System, Man and Cybernetic, vol. 6, no. 4, pp. 267-285, 1976.
[16] T. Randen and J. H. Husoy, "Filtering for texture classification: a comparative study," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 291-310, Apr. 1999.
[17] T. Maenpaa, M. Pietikainen, and T. Ojala, "Texture classification by multi-predicate local binary pattern operators," in Proc. 15th Int. Conf. on Pattern Recognition, 3:951 - 954., vol. 3, pp. 939-942, Barcelona, Spain, 2000.
[18] P. P. Koltsov, "Comparative study of texture detection and classification algorithms," Computational Mathematics and Mathematical Physics, vol. 51, no. 8, pp. 1460-1466, Aug. 2011.
[19] F. Tajeripour, E. Kabir, and A. Sheikhi, "Fabric defect detection using Modified Local Binary Patterns," EURASIP J. on Advances in Signal Processing, vol. 8, pp. 1-12, 2008.
[20] F. Bianconi and A. Fernandez, "On the occurrence probability of local binary patterns: a theoretical study," J. of Mathematical Imaging and Vision Manuscript, vol. 40, no. 3, pp. 259-268, 2011.
[21] ف. تاجریپور، ا. ا. کبیر و ع. شیخی، "آشکارسازی عیوب بافتی با استفاده از شکل بهبودیافته الگوی باینری محلی،" نشریه مهندسی برق و مهندسی کامپیوتر ایران، جلد 5، شماره 3، صص. 128-119، پاییز 1386.
[22] T. Ojala, et al., "Outex - a new framework for empirical evaluation of texture analysis algorithms," in Proc. of 16th Int. Conf. of Pattern Recognition, 2002.
[23] P. Brodatz, Textures: A Photographic Album for Artist and Designer, Dover, New York, 1966.
[24] Z. Guo, L. Zhang, and D. Zheng, "Rotation invariant texture classification using LBP variance (LBPV) using global matching," Pattern Recognition, vol. 43, no. 3, pp. 706-719, Mar. 2010.