Skip to main content
SpringerLink
Log in

Inter Intensity and Color Channel Co-occurrence Histogram for Color Texture Classification

  • Conference paper
  • First Online:
Cognitive Computing and Information Processing (CCIP 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 801))

  • 1154 Accesses

Abstract

In this paper we propose a new method to analyze the color texture image based on inter intensity and color channel co-occurrence histogram, which characterizes the color texture more effectively. This corresponds to the relationships between intensity and color channel along with their neighboring pixels. The proposed color texture descriptor is experimented on VisTex texture dataset. The results are analyzed and compared with Local Binary Patterns (LBP) method and Histogram ratio method. The computational intelligence-based approach, namely, fuzzy classification is used for texture classification. The proposed descriptors achieve better classification results when compared with other two methods. The proposed color texture descriptors are sufficiently robust and precise to distinguish images of different textures even if the sample size is small. The results suggest that the proposed color texture descriptors have the potential for use in real-world applications involving recognition of patterns in digital images.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Haralic, R.M., Shanmugam, K.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)

    Article  Google Scholar 

  2. Wang, L., Liu, J.: Texture classification using multiresolution Markov random field models. Pattern Recogn. Lett. 20(2), 171–182 (1999)

    Article  Google Scholar 

  3. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  4. Jain, A.K., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recogn. 24(12), 1167–1186 (1991)

    Article  Google Scholar 

  5. Wang, J.W., Chen, C.H., Chien, W.M., Tsai, C.M.: Texture classification using non-separable two-dimensional wavelets. Pattern Recogn. Lett. 19(13), 1225–1234 (1998)

    Article  Google Scholar 

  6. Backes, A.R., Casanova, D., Bruno, O.M.: Color texture analysis based on fractal descriptors. Pattern Recogn. 45(5), 1984–1992 (2012)

    Article  Google Scholar 

  7. Paschos, G.: Perceptually uniform color spaces for color texture analysis: an empirical evaluation. IEEE Trans. Image Process. 10(6), 932–937 (2001)

    Article  Google Scholar 

  8. Drimbarean, A., Whelan, P.F.: Experiments in colour texture analysis. Pattern Recogn. Lett. 22(10), 1161–1167 (2001)

    Article  Google Scholar 

  9. Maenpaa, T., Pietikainen, M.: Classification with color and texture: jointly or separately? Pattern Recogn. 37(8), 1629–1640 (2004)

    Article  Google Scholar 

  10. Paschos, G., Petrou, M.: Histogram ratio features for color texture classification. Pattern Recogn. Lett. 24(1–3), 309–314 (2003)

    Article  Google Scholar 

  11. Khan, F.S., Anwer, R.M., van de Weijer, J., Felsberg, M., Laaksonen, J.: Compact color-texture description for texture classification. Pattern Recogn. Lett. 51, 16–22 (2015)

    Article  Google Scholar 

  12. Bianconi, F., Harvey, R., Southam, P., Fernandez, A.: Theoretical and experimental comparision of different approaches for color texture classification. J. Electron. Imaging 20(4), 043006 (2011)

    Article  Google Scholar 

  13. Cusano, C., Napoletano, P., Schettini, R.: Combining multiple features for color texture classification. J. Electron. Imaging 25(6), 061410 (2016)

    Article  Google Scholar 

  14. Palm, C.: Color texture classification by integrative co-occurrence matrices. Pattern Recogn. 37(5), 965–976 (2004)

    Article  Google Scholar 

  15. Kukkonen, S., Kailviaiinen, H., Parkkinen, J.: Color features for quality control in ceramic tile industry. Opt. Eng. 40(2), 170–177 (2001)

    Article  Google Scholar 

  16. Paschos, G.: Fast color texture recognition using chromaticity moments. Pattern Recogn. Lett. 21(9), 837–841 (2000)

    Article  Google Scholar 

  17. Hoang, M.A., Geusebroek, J.M., Smeulders, A.W.: Color texture measurement and segmentation. Signal Process. 85(2), 265–275 (2005)

    Article  Google Scholar 

  18. Jain, A., Healey, G.: A multiscale representation including opponent color features for texture recognition. IEEE Trans. Image Process. 7(1), 124–128 (1998)

    Article  Google Scholar 

  19. Maenpaa, T., Pietikainen, M., Viertola, J.: Separating color and pattern information for color texture discrimination. In: Proceedings of the 16th IEEE International Conference on Pattern Recognition, vol. 1, pp. 668–671 (2002)

    Google Scholar 

  20. Rosenfeld, A., Wang, C.Y., Wu, A.Y.: Multispectral texture. IEEE Trans. Syst. Man Cybern. SMC-12(1), 79–84 (1982)

    Google Scholar 

  21. Vadivel, A., Sural, S., Majumdar, A.K.: An integrated color and intensity co-occurrence matrix. Pattern Recogn. Lett. 28(8), 974–983 (2007)

    Article  Google Scholar 

  22. VisTex: Vision texture database of MIT media lab (1995). http://www-white.media.mit.edu/vismod/imagery/VisionTexture/vistex.html

  23. Cateni, S., Colla, V., Vannucci, M., Borselli, A.: Fuzzy inference systems applied to image classification in the industrial field. In: Fuzzy Inference System-Theory and Applications. InTech (2012)

    Google Scholar 

  24. Bezdek, J.C.: Computing with uncertainty. IEEE Commun. Mag. 30(9), 24–36 (1992)

    Article  Google Scholar 

  25. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, Cambridge (2013)

    MATH  Google Scholar 

  26. Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall PTR, Upper Saddle River (1994)

    MATH  Google Scholar 

  27. Wang, F.: Fuzzy supervised classification of remote sensing images. IEEE Trans. Geosci. Remote Sens. 28(2), 194–201 (1990)

    Article  Google Scholar 

  28. Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 15(4), 580–585 (1985)

    Article  Google Scholar 

Download references

Acknowledgments

We are thankful to referees for their helpful comments and suggestions.

Author information

Authors and Affiliations

  1. Karnatak University, Dharwad, 580003, Karnataka, India

    S. Shivashankar & Madhuri R. Kagale

  2. KLE Technological University, BVBCET, Hubballi, 580031, Karnataka, India

    Prakash S. Hiremath

Authors
  1. S. Shivashankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Madhuri R. Kagale
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prakash S. Hiremath
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Madhuri R. Kagale .

Editor information

Editors and Affiliations

  1. Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India

    T.N. Nagabhushan

  2. Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India

    V. N. Manjunath Aradhya

  3. JSS Academy of Technical Education, Bengaluru, Karnataka, India

    Prabhudev Jagadeesh

  4. JSS Academy of Technical Education, Noida, Uttar Pradesh, India

    Seema Shukla

  5. JSS Academy of Technical Education, Bengaluru, Karnataka, India

    Chayadevi M.L.

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shivashankar, S., Kagale, M.R., Hiremath, P.S. (2018). Inter Intensity and Color Channel Co-occurrence Histogram for Color Texture Classification. In: Nagabhushan, T., Aradhya, V.N.M., Jagadeesh, P., Shukla, S., M.L., C. (eds) Cognitive Computing and Information Processing. CCIP 2017. Communications in Computer and Information Science, vol 801. Springer, Singapore. https://doi.org/10.1007/978-981-10-9059-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-9059-2_17

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-9058-5

  • Online ISBN: 978-981-10-9059-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation