Skip to main content
SpringerLink
Log in

Entropy-Scale Profiles for Texture Segmentation

  • Conference paper
Book cover Scale Space and Variational Methods in Computer Vision (SSVM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6667))

Abstract

We propose a variational approach to unsupervised texture segmentation that depends on very few parameters and is robust to imaging conditions. First, the uneven illumination in the observed image is removed by the proposed image decomposition model that approximates the illumination and well retains the textures and features in the image. Then, from the obtained intrinsic image, we introduce a new data, multiscale local entropy, which is the entropy of each location’s neighborhood histogram with various scales. The proposed segmentation model uses multiscale local entropy as data. Together with a length penalizing term, minimizing the energy functional locates the contours so that the local entropy within each region is similar to one another. Since entropy is the only feature, there are very few parameters. Moreover, the segmentation model can be solved by a fast global minimization method. Experimental results on natural images show the proposed method is able to robustly segment various texture patterns with uneven illumination in the original 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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42(5), 577–685 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  2. Ambrosio, L., Tortorelli, V.M.: Approximation of functionals depending on jumps by elliptic functionals via gamma convergence. Comm. on Pure and Applied Math. 43, 999–1036 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  3. Chan, T., Vese, L.A.: Active contours without edges. IEEE Trans. on Image Processing 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  4. Jain, A.K., Farrakhonia, F.: Unsupervised texture segmentation using gabor filters. Pattern Recognition 23(12), 1167–1186 (1991)

    Article  Google Scholar 

  5. Chan, T., Sandberg, B., Vese, L.: Active contours without edges for textured images. UCLA Comput. Appl. Math. Rep. 02-39 (2002)

    Google Scholar 

  6. Sagiv, C., Sochen, N.A., Zeevi, Y.Y.: Integrated active contours for texture segmentation. IEEE Transactions on Image Processing 15(6), 1633–1646 (2006)

    Article  Google Scholar 

  7. Portilla, J., Simoncelli, E.P.: A parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision 40(1)

    Google Scholar 

  8. Manjunath, B.S., Chellappa, R.: Unsupervised texture segmentation using markov random field models. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(5), 478–482 (1991)

    Article  Google Scholar 

  9. Zhu, S.C., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 18(9), 884–900 (1996)

    Article  Google Scholar 

  10. Yezzi, J.A., Tsai, A., Willsky, A.: A statistical approach to snakes for bimodal and trimodal imagery. In: Int. Conf. on Computer Vision, pp. 898–903 (1999)

    Google Scholar 

  11. Paragios, N., Deriche, R.: Geodesic active regions and level set methods for supervised texture segmentation. International Journal of Computer Vision 46(3)

    Google Scholar 

  12. Rousson, M., Brox, T., Deriche, R.: Active unsupervised texture segmentation on a diffusion based feature space. In: Computer Vision and Pattern Recognition

    Google Scholar 

  13. Aubert, G., Barlaud, M., Faugeras, O., Jehan-Besson, S.: Image segmentation using active contours: Calculus of variations or shape gradients? SIAM Appl. Math. 1(2), 2128–2145 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  14. Herbulot, A., Jehan-Besson, S., Duffner, S., Barlaud, M., Aubert, G.: Segmentation of vectorial image features using shape gradients and information measures. J. Math. Imaging and Vision 25(3), 365–386 (2006)

    Article  MathSciNet  Google Scholar 

  15. Kim, J., Fisher, J.W., Yezzi, A., Cetin, M., Willsky, A.S.: A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Trans. on Image Processing 14, 1486–1502 (2005)

    Article  MathSciNet  Google Scholar 

  16. Awate, S.P., Tasdizen, T., Whitaker, R.T.: Unsupervised texture segmentation with nonparametric neighborhood statistics. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 494–507. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  17. Michailovich, O., Rathi, Y., Tannenbaum, A.: Image segmentation using active contours driven by the bhattacharya gradient flow. IEEE Trans. on Image Processing 16(11), 2787–2801 (2007)

    Article  MathSciNet  Google Scholar 

  18. Ni, K., Bresson, X., Chan, T., Esedoglu, S.: Local histogram based segmentation using the wasserstein distance. International Journal of Computer Vision 84(1), 97–111 (2009)

    Article  Google Scholar 

  19. Li, C., Li, F., Kao, C.-Y., Xu, C.: IImage Segmentation with Simultaneous Illumination and Reflectance Estimation: An Energy Minimization Approach. In: Proc. of ICCV (2009)

    Google Scholar 

  20. Horn, B.K.P.: Robot Vision. MIT Press, Cambridge (1986)

    Google Scholar 

  21. Chen, T., Yin, W., Zhou, X.S., Comaniciu, D., Huang, T.S.: Total variation models for variable lighting face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(9), 1519–1524 (2006)

    Article  Google Scholar 

  22. Rudin, L., Lions, L., Osher, S.: Multiplicative denoising and deblurring: theory and algorithms, vol. 445. Springer, Heidelberg (2003)

    Google Scholar 

  23. Le, T.M., Vese, L.A.: Additive and Multiplicative piecewise-smooth segmentation models in a functional minimization approach. Contemporary Mathematics (2007)

    Google Scholar 

  24. Aujol, J.F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition - modeling, algorithms, and parameter selection. International Journal of Computer Vision 67(1), 111–136 (2006)

    Article  MATH  Google Scholar 

  25. Chan, T., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal on Applied Mathematics 66(5), 1632–1648 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  26. Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J.P., Osher, S.: Fast global minimization of the active contour/snake model. Journal of Mathematical Imaging and Vision 28(2), 151–167 (2007)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea

    Byung-Woo Hong

  2. School of Mathematical and Statistical Sciences, Arizona State University, USA

    Kangyu Ni

  3. Computer Science Department, University of California, Los Angeles, CA, USA

    Stefano Soatto

Authors
  1. Byung-Woo Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kangyu Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefano Soatto
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science Department, Technion — Israel Institute of Technology, 718 Taub Building Technion, 32000, Haifa, Israel

    Alfred M. Bruckstein

  2. Department of Biomedical Engineering, Biomedical Image Analysis and Interpretation, Eindhoven University of Technology, Den Dolech 2 – WH 2.101, 5600 MB, Eindhoven, The Netherlands

    Bart M. ter Haar Romeny

  3. Faculaty of Engineering, School of Electrical Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Alexander M. Bronstein

  4. Institute of Computationa Science, Faculty of Informatics SI-109, Università della Svizzera Italiana, Via Giuseppe Buffi 13, 6904, Lugano, Switzerland

    Michael M. Bronstein

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hong, BW., Ni, K., Soatto, S. (2012). Entropy-Scale Profiles for Texture Segmentation. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-24785-9_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24784-2

  • Online ISBN: 978-3-642-24785-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation