Classification of Textures based on Circular and Elliptical Weighted Symmetric Texture Matrix

  • Authors

    • J Srinivas
    • Ahmed Abdul Moiz Qyser
    • B Eswara Reddy
    https://doi.org/10.14419/ijet.v7i3.27.18503
  • Texture descriptors, rotation invariance, isotropic and anisotropic structures.
  • The Local binary patterns (LBP) derive most efficient high-performance texture features. However, the LBP method derives only isotropic structural features and is unable to capture anisotropic structural information. The elliptical LBP(ELBP) captures only anisotropic information. The LBP, ELBP and its variants derives a wide range of histograms and thus not suitable to integrate second order statistics. To best address this disadvantage, in this paper, we introduce novel descriptors for texture classification, the circular and elliptical weighted symmetric texture matrix (CEWSTM) and robust CEWSTM (RCEWSTM). Different from the traditional LBP, many LBP variants and ELBP, CEWSTM derives a weighted symmetric relationship among the sampling points of circular and elliptical neighborhood (CEN).The CEWSTM computes a texture matrix by efficiently deriving a co-occurrence matrix and its features on weighted center symmetric codes of CEN which can capture microstructure texture information of isotropic and anisotropic structurers. A comprehensive evaluation on benchmark data sets reveals CEWSTM’s high performance, robust to gray scale variations, rotation changes but at a low computational cost.

     

     

  • References

    1. [1] M. Pietikänen, A. Hadid, G. Zhao, and T. Ahonen, Computer Vision Using Local Binary Patterns. London, U.K.: Springer, 2011.

      [2] M. Pietikäinen and G. Zhao, “Two decades of local binary patterns: A survey,†in Advances in Independent Component Analysis and Learning Machines. Amsterdam, The Netherlands: Elsevier, 2015.

      [3] U. Kandaswamy, S. A. Schuckers, and D. Adjeroh, “Comparison of texture analysis schemes under nonideal conditions,†IEEE Trans. Image Process., vol. 20, no. 8, pp. 2260–2275, Aug. 2011.

      [4] J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, “Local features and kernels for classification of texture and object categories: A comprehensive study,†Int. J. Comput. Vis., vol. 73, no. 2, pp. 213–238, Jun. 2007.

      [5] S. Brahnam, L. C. Jain, L. Nanni, and A. Lumini, Eds., Local Binary Patterns: New Variants and Applications. London, U.K.: Springer, 2014.

      [6] T. Ojala, M. Pietikäinen, and T. Maenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, Jul. 2002.

      [7] T. Ahonen, A. Hadid, and M. Pietikäinen, “Face description with local binary patterns: Application to face recognition,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037–2041, Dec. 2006.

      [8] G. Zhao and M. Pietikäinen, “Dynamic texture recognition using local binary patterns with an application to facial expressions,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 6, pp. 915–928, Jun. 2007.

      [9] T. Ahonen, J. Matas, C. He, and M. Pietikäinen, “Rotation invariant image description with local binary pattern histogram Fourier features,†in Proc. Scandinavian Conf. Image Anal., 2009, pp. 61–70.

      [10] L. Liu, L. Zhao, Y. Long, G. Kuang, and P. Fieguth, “Extended local binary patterns for texture classification,†Image Vis. Comput., vol. 30, no. 2, pp. 86–99, Feb. 2012.

      [11] X. Qi, R. Xiao, C.-G. Li, Y. Qiao, J. Guo, and X. Tang, “Pairwise rotation invariant co-occurrence local binary pattern,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 11, pp. 2199–2213, Nov. 2014.

      [12] X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,†IEEE Trans. Image Process., vol. 19, no. 6, pp. 1635–1650, Jun. 2010.

      [13] Y. Guo, G. Zhao, and M. Pietikäinen, “Discriminative features for texture description,†Pattern Recognit., vol. 45, no. 10, pp. 3834–3843, Oct. 2012.

      [14] L. Liu, L. Zhao, Y. Long, G. Kuang, and P. Fieguth, “Extended local binary patterns for texture classification,†Image Vis. Comput., vol. 30, no. 2, pp. 86–99, Feb. 2012.

      [15] Z. Guo, L. Zhang, and D. Zhang, “A completed modeling of local binary pattern operator for texture classification,†IEEE Trans. Image Process., vol. 19, no. 6, pp. 1657–1663, Jun. 2010

      [16] X. Qi, R. Xiao, C.-G. Li, Y. Qiao, J. Guo, and X. Tang, “Pairwise rotation invariant co-occurrence local binary pattern,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 11, pp. 2199–2213, Nov. 2014.

      [17] S. Liao, M. W. K. Law, and A. Chung, “Dominant local binary patterns for texture classification,†IEEE Trans. Image Process., vol. 18, no. 5, pp. 1107–1118, May 2009.

      [18] V. Ojansivu, E. Rahtu, and J. Heikkilä, “Rotation invariant local phase quantization for blur insensitive texture analysis,†in Proc. IEEE Int. Conf. Pattern Recognit. (ICPR), Dec. 2008, pp. 1–4.

      [19] D. K. Iakovidis, E. G. Keramidas, and D. Maroulis, “Fuzzy local binary patterns for ultrasound texture characterization,†in Image Analysis and Recognition (Lecture Notes in Computer Science), A. Campilho and M. Kamel, Eds. Berlin, Germany: Springer, 2008, pp. 750–759.

      [20] J. Chen, V. Kellokumpu, G. Zhao, and M. Pietikänen, “RLBP: Robust local binary pattern,†in Proc. Brit. Vis. Conf. Comput. Vis. (BMVC), 2013, pp. 1–10.

      [21] Hafiane, G. Seetharaman, and B. Zavidovique, “Median binary pattern for textures classification,†in Proc. 4th Int. Conf. Image Anal. Recognit., 2007, pp. 387–398.

      [22] Fathi and A. R. Naghsh-Nilchi, “Noise tolerant local binary pattern operator for efficient texture analysis,†Pattern Recognit. Lett., vol. 33, no. 9, pp. 1093–1100, Jul. 2012.

      [23] J. Ren, X. Jiang, and J. Yuan, “Noise-resistant local binary pattern with an embedded error-correction mechanism,†IEEE Trans. Image Process., vol. 22, no. 10, pp. 4049–4060, Oct. 2013

      [24] T.Ahonen,A. Hadid,M. Pietikäinen, Face recognition with local binary patterns, :Proceedings of Eighth European Conference on Computer Vision, 2004,pp.469–481.

      [25] Z. Lei, S.Liao, M.Pietikäinen, S.Z.Li, Face recognition by exploring information jointly in space, scale and orientation, IEEE Trans. ImageProcess.20(1)(2011) 247–256.

      [26] T.Jabid, M.H.Kabir, O.Chae, Local directional pattern(LDP)-a robust image descriptor for Object Recognition, in: Proceedings of 7th IEEE International Conference on Advanced Video and Signal Based Surveillance,2010,pp.482–487

      [27] Lakhdar Belhallouche ,, Kamel Belloulata, Kidiyo Kpalma , A New Approach to Region Based Image Retrieval using Shape Adaptive Discrete Wavelet Transform, I.J. Image, Graphics and Signal Processing, 2016, 1, 1-14.

      [28] Pranoti P. Mane , Narendra G. Bawane , Image Retrieval by Utilizing Structural Connections within an Image , I.J. Image, Graphics and Signal Processing, 2016, 1, 68-74.

      [29] K. Prasanthi Jasmine1 ; P. Rajesh Kumar2 , Color and Rotated M-Band Dual Tree Complex Wavelet Transform Features for Image RetrievalM. I.J. Image, Graphics and Signal Processing, 2014, 9, 1-10.

      [30] X. Huang, S.Z. Li, Y. Wang, Shape localization based on statistical method using extended local binary patterns, in: Proc. Inter. Conf. Image and Graphics, Beijing, China, 2004, pp. 184–187.

      [31] Ding, C. Xu, and D. Tao, “Multi-task pose-invariant face recognition,†IEEE Trans. Image Process., vol. 24, no. 3, pp. 980–993, Mar. 2015.

      [32] V. Vijaya Kumar, K. Srinivasa Reddy, V. Venkata Krishna “Face Recognition Using Prominent LBP Modelâ€, International Journal of Applied Engineering Research , Vol. 10, Iss. 2, 2015, pp. 4373-4384, ISSN: 0973-4562

      [33] M. Heikkila, M. Pietikainen, A texture based method for modeling the background and detecting moving objects, IEEE Trans. Pattern Anal. Mach.Intell. 28 (4) (2006) 657–662.

      [34] Paweł Tarnowski, Marcin Kołodziej, Andrzej Majkowski, Remigiusz J. Rak, Emotion recognition using facial expressions, Procedia Computer Science 108C (2017) 1175–1184.

      [35] Guo Xianhai, Study of Emotion Recognition Based on Electrocardiogram and RBF neural network, Advanced in Control Engineering and Information Science, Procedia Engineering 15 (2011) 2408 – 2412.

      [36] M. Heikkila, M. Pietikainen, C. Schmid, Description of interest regions with local binary patterns, Pattern Recogn. 42 (2009) 425–436.

      [37] M. Li, R.C. Staunton, Optimum Gabor filter design and local binary patterns for texture segmentation, Elsevie J. Pattern Recogn. 29 (2008) 664–672.

      [38] K. Subba Reddy, V. Vijaya Kumar, A.P. Siva Kumar, Classification of Textures Using a New Descriptor Circular and EllipticalLBP (CE-ELBP), International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 19 (2017) pp. 8844-8853

      [39] K. Subba Reddy, V. Vijaya Kumar, A.P. Siva Kumar, Cross Diagonal Circular and Elliptical Texture Matrix For Efficient Texture Classification, Journal of Advanced Research in Dynamical and Control Systems, (Accepted).

      [40] Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognit. 42(3), 425–436 (2009).

      [41] Haralick RM , Shanmugan K and Dinstein I, "Textural features for image classification", IEEE Trans. Sysr., Man., Cybern., Vol. SMC-3, no. 6, pp. 610-621, 1973.

      [42] Zhao G, Ahonen T, Matas J, Pietikainen M (2011) Rotation-invariant image and video description with local binary pattern features. IEEE Transactions on Image Processing 21(4): 1465–1477

      [43] Marko Heikkil¨a, Matti Pietik¨ainen, and Cordelia Schmid, Description of Interest Regions with Center-Symmetric Local Binary Patterns, P. Kalra and S. Peleg (Eds.): ICVGIP 2006, LNCS 4338, pp. 58–69, 2006

      [44] P. Brodatz, Textures: A Photographic Album for Artists and Designers. New York, NY, USA: Dover, 1996.

      [45] S. Lazebnik, C. Schmid, and J. Ponce, “A sparse texture representation using local affine regions,†IEEE Trans. Pattern Anal. Mach. Intell.,vol. 27, no. 8, pp. 1265–1278, Aug. 2005

      [46] http://www.outex.oulu.fi/index.php?page=image_databaseS.

      [47] E. Hayman, B. Caputo, M. Fritz, and J. Eklundh, “On the significance of real-world conditions for material classification,†in European Conference on Computer Vision (ECCV), 2004, pp. 253–266

      [48] G. J. Burghouts and J.-M. Geusebroek, “Material-specific adaptation of color invariant features,†Pattern Recognit. Lett., vol. 30, no. 3,pp. 306–313, Feb. 2009.

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  • How to Cite

    Srinivas, J., Abdul Moiz Qyser, A., & Eswara Reddy, B. (2018). Classification of Textures based on Circular and Elliptical Weighted Symmetric Texture Matrix. International Journal of Engineering & Technology, 7(3.27), 593-600. https://doi.org/10.14419/ijet.v7i3.27.18503