Skip to main content
SpringerLink
Log in

Color and multiscale texture features from vectorial mathematical morphology

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this paper, we explore an original way to compute texture features for color images in a vector process. Using a dedicated approach for color ordering, we produce a complete framework for color mathematical morphology adapted to human visual system characteristics. Then, morphological multiscale texture features are defined. To understand the texture feature behavior, we present the feature response to basic images variations. Finally, we compare the texture feature performance in front of a classical classification task using Outex database.

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

Access this article

Log in via an institution

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
Fig. 9

Similar content being viewed by others

Notes

  1. The contrast is the distance/difference between two values.

  2. The number of patterns per line is equal to the number of patterns per column.

References

  1. Ledoux, A., Richard, N., Capelle-Laizé, A.S., et al.: The fractal estimator: a validation criterion for the colour mathematical morphology. In: 6th European Conference on Colour in Graphics, Imaging, and Vision, pp. 206–210 (2012)

  2. Angulo-Lopez, J., Serra, J.: Modelling and segmentation of colour images in polar representations. Image Vis. Comput. 25(4), 475–495 (2007)

    Article  Google Scholar 

  3. Aptoula, E., Sébastien, L., et al.: On morphological color texture characterization. In: Proceedings of the International Symposium on Mathematical Morphology (ISMM), pp. 153–164 (2007)

  4. Matheron, G.: Random Sets and Integral Geometry. Wiley, New York (1975)

    MATH  Google Scholar 

  5. Sternberg, S.R.: Grayscale morphology. Image Underst. Comput. Vis. Graph. Image Process. 35(3), 33–355 (1986)

    MathSciNet  Google Scholar 

  6. Heijmans, H.J.A.M., Ronse, C.: The algebraic basis of mathematical morphology: I. Dilations and erosions. Image Underst. Comput. Vis. Graph. Image Process. 50(3), 245–295 (1990)

    Article  MATH  Google Scholar 

  7. Serra, J.: Image Analysis and Mathematical Morphology, vol. I. Academic Press, New York (1982)

    MATH  Google Scholar 

  8. Aptoula, E.: Comparative study of moment based parameterization for morphological texture description. J. Vis. Commun. Image Represent. 23(8), 1213–1224 (2012)

    Article  Google Scholar 

  9. Mandelbrot, B.B.: Fractals: Form, Chance, and Dimension. W.H. Freeman and Company, London (1977)

    MATH  Google Scholar 

  10. Peleg, S., Naor, J., Hartley, R., et al.: Multiple résolution texture analysis and classification. IEEE PAMI 6, 518–523 (1984)

    Article  Google Scholar 

  11. Louverdis, G., Vardavoulia, M.I., Andreadis, I., et al.: A new approach to morphological color image processing. Pattern Recognit. 35(8), 1733–1741 (2002)

    Article  MATH  Google Scholar 

  12. Hanbury, A., Serra, J.: Morphological operators on the unit circle. IEEE Trans. Image Process. 10(12), 1842–1850 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  13. Ledoux, A., Richard, N., Capelle-Laizé, A.S., et al.: Limitations et comparaisons d’ordonnancement utilisant des distances couleur. In: XXIIIe Colloque GRETSI (Groupe d’Etudes du Traitement du Signal et des Images) (2011)

  14. CIE. Methods for deriving colour differences in images. CIE-technical report, number:18x:2008 (2008). ISBN: 978-3-901906-xx-yy

  15. Ledoux, A.: Vers des traitements morphologiques couleur et spectraux valides au sens perceptuel et physique: Méthodes et criteres de sélection. Université de Poitiers, Poitiers (2013)

    Google Scholar 

  16. Ojala, T., Maenpaa, T., Pietikinenand M., et al.: Outex-new framework for empirical evaluation of texture analysis algorithms. In: 16th International Conference on Pattern Recognition, vol. 1, pp. 701–706 (2002)

  17. Arvis, V., Debain, C., Berducat, M., Benassi, A.: Generalization of the cooccurrence matrix for colour images: application to colour texture classification. Image Anal. Stereol. 23(1), 63–72 (2004)

    Article  Google Scholar 

  18. Ojala, T., Pietikinen, M., Menp, 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 

  19. Martínez-Rios, R.A., Richard, N., Ledoux, A., and al.: Colour texture classification and spatio-chromatic complexity. X Colore conferenza (2014)

  20. Martínez-Rios, R.A., Richard, N., Fernandez-Maloigne, C.: Alternative to colour feature classification using colour contrast occurrence matrix. In: Proceedings of the International Conference on Quality Control by Artificial Vision (2015)

Download references

Author information

Authors and Affiliations

  1. XLIM-SIC JUR CNRS 7252, University of Poitiers, Bât. SP2MI, Bd Marie et Pierre Curie, Téléport 2, 86962, Futuroscope Cedex, France

    A. Ledoux & N. Richard

Authors
  1. A. Ledoux
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. N. Richard
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Ledoux.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ledoux, A., Richard, N. Color and multiscale texture features from vectorial mathematical morphology. SIViP 10, 431–438 (2016). https://doi.org/10.1007/s11760-015-0759-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-015-0759-3

Keywords

Search

Navigation