article

A Statistical Approach to Texture Classification from Single Images

Authors:

Andrew ZissermanAuthors Info & Claims

International Journal of Computer Vision, Volume 62, Issue 1-2

Pages 61 - 81

https://doi.org/10.1007/s11263-005-4635-4

Published: 01 April 2005 Publication History

Abstract

We investigate texture classification from single images obtained under unknown viewpoint and illumination. A statistical approach is developed where textures are modelled by the joint probability distribution of filter responses. This distribution is represented by the frequency histogram of filter response cluster centres (textons). Recognition proceeds from single, uncalibrated images and the novelty here is that rotationally invariant filters are used and the filter response space is low dimensional.

Classification performance is compared with the filter banks and methods of Leung and Malik [ IJCV , 2001], Schmid [ CVPR , 2001] and Cula and Dana [ IJCV , 2004] and it is demonstrated that superior performance is achieved here. Classification results are presented for all 61 materials in the Columbia-Utrecht texture database.

We also discuss the effects of various parameters on our classification algorithm--such as the choice of filter bank and rotational invariance, the size of the texton dictionary as well as the number of training images used. Finally, we present a method of reliably measuring relative orientation co-occurrence statistics in a rotationally invariant manner, and discuss whether incorporating such information can enhance the classifier's performance.

References

[1]

Brodatz, P. 1966. Textures: A Photographic Album for Artists & Designers . Dover: New York.

[2]

Chantler, M.J., McGunnigle, G., Penirschke, A., and Petrou, M. 2002a. Estimating lighting direction and classifying textures. In Proceedings of the 13th British Machine Vision Conference . Cardiff, pp. 737-746.

[3]

Chantler, M.J., McGunnigle, G., and Wu, J. 2000. Surface rotation invariant texture classification using photometric stereo and surface magnitude spectra. In Proceedings of the 11th British Machine Vision Conference . Bristol, pp. 486-495.

[4]

Chantler, M.J., Schmidt, M., Petrou, M., and McGunnigle, G. 2002b. The effect of illuminant rotation on texture filters: Lissajous's ellipses. In Proceedings of the 7th European Conference on Computer Vision . Copenhagen, Denmark, pp. 289-303.

[5]

Cristianini, N. and Shawe-Taylor, J. 2000. An Introduction to Support Vector Machines . Cambridge University Press.

[6]

Cula, O.G. and Dana, K.J. 2004. 3D Texture recognition using bidirectional feature histograms. International Journal of Computer Vision , 59(1).

[7]

Dana, K.J. and Nayar, S. 1998. Histogram model for 3d textures. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 618-624.

Digital Library

[8]

Dana, K.J. and Nayar, S. 1999. Correlation model for 3D texture. In: Proceedings of the International Conference on Computer Vision , pp. 1061-1067.

[9]

Dana, K.J., van Ginneken, B., Nayar, S.K., and Koenderink, J.J. 1999. Reflectance and texture of real world surfaces. ACM Transactions on Graphics , 18(1): 1-34.

Digital Library

[10]

Duda, R.O., Hart, P.E., and Stork, D.G. 2001. Pattern Classification . John Wiley and Sons, 2nd edition.

[11]

Fowlkes, C., Martin, D., Ren, X., and Malik, J. 2002. Detecting and localizing boundaries in natural images. Technical report, University of California at Berkeley.

[12]

Funt, B., Barnard, K., and Martin, L. 1998. Is colour constancy good enough?. In Proceedings of the European Conference on Computer Vision . Springer-Verlag, pp. 445-459.

[13]

Gates, G.W. 1972. The reduced nearest neighbour rule. IEEE Transactions on Information Theory , 18(3):431-433.

Digital Library

[14]

Hayman, E., Caputo, B., Fritz, M., and Eklundh, J. 2004. On the significance of real-world conditions for material classification. In Proceedings of the European Conference on Computer Vision to appear.

[15]

Kaufman, L. and Rousseeuw, P.J. 1990. Finding Groups in Data: An Introduction to Cluster Analysis . John Wiley & Sons, NY, USA.

[16]

Kim, K.I., Jung, K., Park, S.H., and Kim, H.J. 2002. Support vector machines for texture classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(11): 1542-1550.

Digital Library

[17]

Konishi, S. and Yuille, A.L. 2000. Statistical cues for domain specific image segmentation with performance analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 125-132.

[18]

Lazebnik, S., Schmid, C., and Ponce, J. 2003a. Affine-invariant local descriptors and neighborhood statistics for texture recognition. In Proceedings of the International Conference on Computer Vision , pp. 649-655.

[19]

Lazebnik, S., Schmid, C., and Ponce, J. 2003b. A sparse texture representation using affine-invariant regions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , vol. 2, pp. 319-324.

[20]

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

Digital Library

[21]

Lindeberg, T. and Gårding, J. 1994. Shape-adapted smoothing in estimation of 3-D depth cues from affine distortions of local 2-D brightness structure. In Proceedings of the 3rd European Conference on Computer Vision . Stockholm, Sweden, pp. 389- 400.

[22]

Malik, J., Belongie, S., Leung, T., and Shi, J. 2001. Contour and texture analysis for image segmentation. International Journal of Computer Vision , 43(1):7-27.

Digital Library

[23]

Penirschke, A., Chantler, M.J., and Petrou, M. 2002. Illuminant rotation invariant classification of 3D surface textures using lissajous's ellipses. In Proceedings of the 2nd International Workshop on Texture Analysis and Synthesis . Copenhagen, Denmark, pp. 103- 108.

[24]

Perona, P. 1992. Steerable-scalable kernels for edge detection and junction analysis. In European Conference on Computer Vision , pp. 3-18.

[25]

Press, W., Teukolsky, S., Vetterling, W., and Flannery, B. 1992. Numerical Recipes in C (2nd ed.). Cambridge University Press.

[26]

Schaffalitzky, F. and Zisserman, A. 2001. Viewpoint invariant texture matching and wide baseline stereo. In Proceedings of the 8th International Conference on Computer Vision . Vancouver, Canada, pp. 636-643.

[27]

Schmid, C. 2001. Constructing models for content-based image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , vol. 2, pp. 39-45.

[28]

Schölkopf, B. and Smola, A. 2002. Learning with Kernels . MIT Press.

[29]

Suen, P. and Healey, G. 2000. The analysis and reconstruction of real-world textures in three dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence , 22(5):491-503.

Digital Library

[30]

Tipping, M. 2001. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research , 1:211-244.

Digital Library

[31]

Toussaint, G. 2002. Proximity graphs for nearest neighbor decision rules: Recent progress. In Interface 2002, 34th Symposium on Computing and Statistics .

[32]

Varma, M. and Zisserman, A. 2002. Classifying images of materials: Achieving viewpoint and illumination independence. In Proceedings of the 7th European Conference on Computer Vision, Copenhagen, Denmark , vol. 3, Springer-Verlag, pp. 255-271.

[33]

Varma, M. and Zisserman, A. 2005. On unifying statistical texture classification frameworks. Image and Vision Computing (to appear).

Cited By

Kříž PHaindl M(2024)Multispectral Texture BenchmarkProcedia Computer Science10.1016/j.procs.2023.10.308225:C(3143-3152)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.10.308
Mo HZhao G(2024)RIC-CNNPattern Recognition10.1016/j.patcog.2023.109994146:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.patcog.2023.109994
Florindo J(2024)Fractal poolingExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122978243:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122978
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Index Terms

A Statistical Approach to Texture Classification from Single Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Appearance and texture representations
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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Information

Published In

cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 62, Issue 1-2

Special Issue on Texture Analysis and Synthesis

April-May 2005

185 pages

ISSN:0920-5691

Issue’s Table of Contents

Copyright © Copyright © 2005 Springer Science+Business Media, Inc.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2005

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Cited By

Kříž PHaindl M(2024)Multispectral Texture BenchmarkProcedia Computer Science10.1016/j.procs.2023.10.308225:C(3143-3152)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1016/j.procs.2023.10.308
Mo HZhao G(2024)RIC-CNNPattern Recognition10.1016/j.patcog.2023.109994146:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.patcog.2023.109994
Florindo J(2024)Fractal poolingExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122978243:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122978
Driouache YMilpied JMotamedi A(2024)Vision-based method to identify materials transported by dump trucksEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108768135:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108768
Liu YDong HWang GChen C(2024)Dual-branch network based on transformer for texture recognitionDigital Signal Processing10.1016/j.dsp.2024.104612153:COnline publication date: 1-Oct-2024
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Florindo JAbreu E(2024)A pseudo-parabolic diffusion model to enhance deep neural texture featuresMultimedia Tools and Applications10.1007/s11042-023-15886-w83:4(11507-11528)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15886-w
Evani RRajan DMao S(2024)Multiscale Graph Texture NetworkComputer Vision – ECCV 202410.1007/978-3-031-73411-3_13(218-235)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-73411-3_13
Florindo JLaureano E(2023)BoFFExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119627219:COnline publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119627
Priya GPadmapriya N(2023)PT-CNN: A Non-linear Lightweight Texture Image ClassifierNeural Processing Letters10.1007/s11063-023-11322-055:6(8483-8507)Online publication date: 9-Jun-2023
https://dl.acm.org/doi/10.1007/s11063-023-11322-0
Lang SZhang JChen FCai YWu Q(2023)Material classification of polishing and convex surface objects based on photon accumulation point spread function (PAPSF) from imaging model of binocular pulsed time-of-flight cameraMachine Vision and Applications10.1007/s00138-022-01366-y34:1Online publication date: 7-Jan-2023
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