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A New Rotation-Invariant Approach for Texture Analysis

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Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 456)


Image processing and pattern recognition are one of the most important area of research in computer science. Recently, several studies have been made and efficient approaches have been proposed to provide efficient solutions to many real and industrial problems. Texture analysis is a fundamental field of image processing because all surfaces of objects are textured in nature. Thus, we proposed a new texture analysis method. In this paper, we proposed a novel texture analysis approach based on a recent feature extraction method called neighbor based binary pattern (NBP). The NBP method extract the local micro texture and is robust against rotation, which is a key problem in image processing. The proposed system extract two-reference NBP histograms from the texture in order to calculate a model of the texture. Finally, several models have been constructed to be able to recognize textures even after rotation. Textured images from Brodatz album database were used in the evaluation. Experimental studies have illustrated that the proposed system obtain very encouraging results robust to rotation compared to classical method.


  • Rotation invariance
  • Texture analysis
  • Feature extraction
  • Neighbor based binary pattern


  1. Richards, W., Polit, A.: Texture matching. Kybernatic 16, 155–162 (1974)

    CrossRef  Google Scholar 

  2. Baohua, Y., Yuan, H., Jiuliang, C.: Combining Local Binary Pattern and Local Phase Quantization for Face Recognition. In: Biometrics and Security Technologies (ISBAST), pp. 51–53 (March 2012)

    Google Scholar 

  3. Jain, A.K., Ross, A., Prabhakar, S.: Fingerprint matching using minutiae and texture features. In: International Conference on Image Processing, vol. 3, pp. 282–285 (2001)

    Google Scholar 

  4. Hamouchene, I., Aouat, S., Lacheheb, H.: Texture Segmentation and Matching Using LBP Operator and GLCM Matrix. In: Chen, L., Kapoor, S., Bhatia, R. (eds.) Intelligent Systems for Science and Information. SCI, vol. 542, pp. 389–407. Springer, Heidelberg (2014)

    CrossRef  Google Scholar 

  5. Harlick, R.: Statistical and structural approaches to texture. Proc. of IEEE 67(5), 786–804 (1979)

    CrossRef  Google Scholar 

  6. Hamouchene, I., Aouat, S.: A New Texture Analysis Approach for Iris Recognition. In: AASRI Conference on Circuit and Signal Processing (CSP 2014), vol. 9, pp. 2–7 (2014)

    Google Scholar 

  7. Hamouchene, I., Aouat, S.: A cognitive approach for texture analysis using neighbors-based binary patterns. In: IEEE 13th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), August 18-20, pp. 94–99 (2014)

    Google Scholar 

  8. Ojala, T., Pietikäinen, M., Harwood, D.: A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition 29, 51–59 (1996)

    CrossRef  Google Scholar 

  9. Ojala, T., Pietikäinen, M.: Unsupervised Texture Segmentation Using Feature Distributions. Pattern Recognition 32, 477–486 (1999)

    CrossRef  Google Scholar 

  10. Guo, Z., Zhang, L., Zhang, D.: A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Transactions on Image Processing 19(6), 1657–1663 (2010)

    CrossRef  MathSciNet  Google Scholar 

  11. Xueming, Q., Xian-Sheng, H., Ping, C., Liangjun, K.: An effective local binary patterns texture descriptor with pyramid representation. Pattern Recognition 44(10-11), 2502–2515 (2011)

    CrossRef  Google Scholar 

  12. Baohua, Y., Yuan, H., Jiuliang, C.: Combining Local Binary Pattern and Local Phase Quantization for Face Recognition. In: Biometrics and Security Technologies (ISBAST), pp. 51–53 (March 2012)

    Google Scholar 

  13. Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1966)

    Google Scholar 

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Correspondence to Izem Hamouchene .

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© 2015 IFIP International Federation for Information Processing

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Hamouchene, I., Aouat, S. (2015). A New Rotation-Invariant Approach for Texture Analysis. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, E., Wrembel, R. (eds) Computer Science and Its Applications. CIIA 2015. IFIP Advances in Information and Communication Technology, vol 456. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19577-3

  • Online ISBN: 978-3-319-19578-0

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