Skip to main content

Color texture recognition by color information fusion using the non-extensive entropy

  • Published:
Multidimensional Systems and Signal Processing Aims and scope Submit manuscript

Abstract

Color textures have a unique inter-relationship among its color planes since they contribute information about the same recurring pattern. The average information or entropy is thus presumed to be redundant across the color planes. This is the basis of our paper, which focuses on dimensionality reduction of color texture features by averaging the entropies across multidimensional color planes, while at the same time maintaining the high accuracy of color texture recognition. The mean operation was used in summarizing the original eleven-dimensional difference theoretic texture features for texture classification in Susan and Hanmandlu (IET Image Process 7(8):725–732, 2013). In this work, instead of the mean, we measure the entropy of the features across multidimensional color planes. The non-extensive entropy with the Gaussian information gain is used as the entropy measure for our experiments since it is non-linear and a good indicator of regular patterns in textures. Comparisons with the state-of-the-art prove the efficiency of our approach both in terms of accuracy and the reduced feature dimension.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Alvarez, S., Salvatella, A., Vanrell, M., & Otazu, X. (2012). Low dimensional and comprehensive color texture recognition. Computer Vision and Image Understanding, 116, 54–67.

    Article  Google Scholar 

  • Alvarez, S., & Vanrell, M. (2012). Texton theory revisited: A bag-of-words approach to combine textons. Pattern Recognition, 45(12), 4312–4325.

    Article  Google Scholar 

  • Ershad, S. F. (2011). Color texture classification approach based on combination of primitive pattern units and statistical features. arXiv preprint arXiv:1109.1133.

  • Estevez, P. A., Tesmer, M., Perez, C. A., & Zurada, J. M. (2009). Normalized mutual information feature selection. IEEE Transactions on Neural Networks, 20(2), 189–201.

    Article  Google Scholar 

  • Griffin, G., Holub, A., & Perona, P. (2007). Caltech-256 object category dataset.

  • Guo, Z., Zhang, L., & Zhang, D. (2010b). A completed modeling of local binary pattern operator for texture classification. IEEE Transactions on Image Processing, 19(6), 1657–1663.

    Article  MathSciNet  MATH  Google Scholar 

  • Guo, Z., Zhang, L., & Zhang, D. (2010a). Rotation invariant texture classification using LBP Variance with global matching. Pattern Recognition, 43, 706–719.

    Article  MATH  Google Scholar 

  • Haralick, R. M., Shanmugan, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3, 610–621.

    Article  Google Scholar 

  • Ilgner, J. F. R., Palm, C., SchSutz, A. G., Spitzer, K., Westhofen, M., & Lehmann, T. M. (2003). Colour texture analysis for quantitative laryngoscopy. Acta Otorrinolaringologica, 123, 730–734.

    Article  Google Scholar 

  • Khan, Fahad Shahbaz, Anwer, Rao Muhammad, Weijer, Joost van de, Felsberg, Michael, & Laaksonen, Jorma. (2015). Compact color-texture description for texture classsssification. Pattern Recognition Letters, 51, 16–22.

    Article  Google Scholar 

  • Leung, T., & Malik, J. (2001). Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision, 43(1), 29–44.

    Article  MATH  Google Scholar 

  • Luukka, P. (2011). Feature selection using fuzzy entropy measures with similarity classifier. Expert Systems with Applications, 38, 4600–4607.

    Article  Google Scholar 

  • Messer, K., & Kittler, J. (1999). A region-based image database system using colour and texture. Pattern Recognition Letters, 20, 1323–1330.

    Article  Google Scholar 

  • Ojala, T., Maenpaa, T., Pietikainen, M., Viertola, J., Kyllonen, J., & Huovinen, S. (2002b). Outex-new framework for empirical evaluation of texture analysis algorithms. In Proceedings of the 16th international conference on pattern recognition, 2002 (Vol. 1, pp. 701–706). IEEE.

  • Ojala, T., Pietikaenen, M., & Maenepae, T. (2002a). Multi-resolution gray scale and rotation invariant texture classification with LBP. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.

    Article  Google Scholar 

  • Palm, C. (2004). Color texture classification by integrative co-occurence matrices. Pattern Recognition, 37, 965–976.

    Article  Google Scholar 

  • Pal, N., & Pal, S. (1991). Entropy—A new definition and its applications. IEEE Transactions on Systems, Man, and Cybernetics, 21(5), 1260–1270.

    Article  MathSciNet  MATH  Google Scholar 

  • Porebski, A., Vandenbrouke, N., & Macaire, L. (2008). Haralick feature extraction from LBP images for color texture classification. In Proceedings of IEEE conference on image processing theory, tools and applications.

  • Renyi, A. (1961). On measures of entropy and information. Fourth Berkeley Symposium, 1, 547–561.

    MathSciNet  MATH  Google Scholar 

  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell system Technical Journal, 27, 379–423.

    Article  MathSciNet  MATH  Google Scholar 

  • Susan, S., & Hanmandlu, M. (2013a). A non-extensive entropy feature and its application to texture classification. Neurocomputing, 120, 214–225.

    Article  Google Scholar 

  • Susan, S., & Hanmandlu, M. (2013b). Difference theoretic feature set for scale-, illumination-and rotation-invariant texture classification. IET Image Processing, 7(8), 725–732.

    Article  Google Scholar 

  • Tan, Xiaoyang, & Triggs, Bill. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19(6), 1635–1650.

    Article  MathSciNet  MATH  Google Scholar 

  • Tsallis, C., Abe, S., & Okamoto, Y. (2001). Non extensive statistical mechanics and its applications. Lecture notes in physics. Berlin: Springer.

    Google Scholar 

  • Varma, M., & Zisserman, A. (2005). A statistical approach to texture classification using single images. International Journal of Computer Vision, 62, 61–81.

    Article  Google Scholar 

  • Zhu, C., Bichot, C.-E., & Chen, L. (2010). Multi-scale color local binary patterns for visual object classes recognition. In ICPR 2010 (pp. 3065–3068).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seba Susan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Susan, S., Hanmandlu, M. Color texture recognition by color information fusion using the non-extensive entropy. Multidim Syst Sign Process 29, 1269–1284 (2018). https://doi.org/10.1007/s11045-017-0502-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11045-017-0502-z

Keywords