Computational Visual Media

, Volume 4, Issue 2, pp 173–184 | Cite as

Automatic texture exemplar extraction based on global and local textureness measures

  • Huisi Wu
  • Xiaomeng Lyu
  • Zhenkun WenEmail author
Open Access
Research Article


Texture synthesis is widely used for modeling the appearance of virtual objects. However, traditional texture synthesis techniques emphasize creation of optimal target textures, and pay insufficient attention to choice of suitable input texture exemplars. Currently, obtaining texture exemplars from natural images is a labor intensive task for the artists, requiring careful photography and significant postprocessing. In this paper, we present an automatic texture exemplar extraction method based on global and local textureness measures. To improve the efficiency of dominant texture identification, we first perform Poisson disk sampling to randomly and uniformly crop patches from a natural image. For global textureness assessment, we use a GIST descriptor to distinguish textured patches from non-textured patches, in conjunction with SVM prediction. To identify real texture exemplars consisting solely of the dominant texture, we further measure the local textureness of a patch by extracting and matching the local structure (using binary Gabor pattern (BGP)) and dominant color features (using color histograms) between a patch and its sub-regions. Finally, we obtain optimal texture exemplars by scoring and ranking extracted patches using these global and local textureness measures. We evaluate our method on a variety of images with different kinds of textures. A convincing visual comparison with textures manually selected by an artist and a statistical study demonstrate its effectiveness.


texture exemplar extraction textureness GIST descriptor binary Gabor pattern (BGP) 



This work was supported in part by grants from the National Natural Science Foundation of China (Nos. 61303101 and 61572328), the Shenzhen Research Foundation for Basic Research, China (Nos. JCYJ20150324140036846, JCYJ20170302153551588, CXZZ20140902160818443, CXZZ20140902102350474, CXZZ20150813151056544, JCYJ20150630105452814, JCYJ20160331114551175, and JCYJ20160608173051207), and the Startup Research Fund of Shenzhen University (No. 2013-827-000009).


  1. [1]
    Tartavel, G.; Gousseau, Y.; Peyré, G. Variational texture synthesis with sparsity and spectrum constraints. Journal of Mathematical Imaging and Vision Vol. 52, No. 1, 124–144, 2015.MathSciNetCrossRefzbMATHGoogle Scholar
  2. [2]
    Gatys, L. A.; Ecker, A. S.; Bethge, M. Texture synthesis using convolutional neural networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, Vol. 1, 262–270, 2015.Google Scholar
  3. [3]
    Aguerrebere, C.; Gousseau, Y.; Tartavel, G. Exemplarbased texture synthesis: The Efros–Leung algorithm. Image Processing on Line Vol. 3, 223–241, 2013.CrossRefGoogle Scholar
  4. [4]
    Lockerman, Y. D.; Xue, S.; Dorsey, J.; Rushmeier, H. Creating texture exemplars from unconstrained images. In: Proceedings of the International Conference on Computer-Aided Design and Computer Graphics, 397–398, 2013.Google Scholar
  5. [5]
    Moritz, J.; James, S.; Haines, T. S. F.; Ritschel, T.; Weyrich, T. Texture stationarization: Turning photos into tileable textures. Computer Graphics Forum Vol. 36, No. 2, 177–188, 2017.CrossRefGoogle Scholar
  6. [6]
    Lu, J.; Dorsey, J.; Rushmeier, H. Dominant texture and diffusion distance manifolds. Computer Graphics Forum Vol. 28, No. 2, 667–676, 2009.CrossRefGoogle Scholar
  7. [7]
    Wang, W.; Hua, M. Extracting dominant textures in real time with multi-scale hue-saturation-intensity histograms. IEEE Transactions on Image Processing Vol. 22, No. 11, 4237–4248, 2013.MathSciNetCrossRefzbMATHGoogle Scholar
  8. [8]
    Lockerman, Y. D.; Sauvage, B.; Allègre, R.; Dischler, J.-M.; Dorsey, J.; Rushmeier, H. Multi-scale label-map extraction for texture synthesis. ACM Transactions on Graphics Vol. 35, No. 4, Article No. 140, 2016.Google Scholar
  9. [9]
    Efros, A. A.; Freeman, W. T. Image quilting for texture synthesis and transfer. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, 341–346, 2001.Google Scholar
  10. [10]
    Wu, Q.; Yu, Y. Feature matching and deformation for texture synthesis. ACM Transactions on Graphics Vol. 23, No. 3, 364–367, 2004.CrossRefGoogle Scholar
  11. [11]
    Campisi, P.; Scarano, G. A multiresolution approach for texture synthesis using the circular harmonic functions. IEEE Transactions on Image Processing Vol. 11, No. 1, 37–51, 2002.CrossRefGoogle Scholar
  12. [12]
    Fišer, J.; Jamriška, O.; Simons, D.; Shachtman, E.; Lu, J.; Asente, P.; Lukáč, M.; Sýkora, D. Examplebased synthesis of stylized facial animations. ACM Transactions on Graphics Vol. 36, No. 4, Article No. 155, 2017.Google Scholar
  13. [13]
    Turk, G. Texture synthesis on surfaces. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, 347–354, 2001.Google Scholar
  14. [14]
    Liu, Y.; Lin, W.-C.; Hays, J. Near-regular texture analysis and manipulation. ACM Transactions on Graphics Vol. 23, No. 3, 368–376, 2004.CrossRefGoogle Scholar
  15. [15]
    Karthlkeyani, V.; Duraiswamy, K.; Kamalakkannan, P. Texture analysis and synthesis for near-regular textures. In: Proceedings of the International Conference on Intelligent Sensing and Information Processing, 134–139, 2005.Google Scholar
  16. [16]
    Lin, W. C.; Hays, J.; Wu, C.; Liu, Y.; Kwatra, V. Quantitative evaluation of near regular texture synthesis algorithms. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 427–434, 2006.Google Scholar
  17. [17]
    Latif-Amet, A.; Ertüzün, A.; Erçil. A. An efficient method for texture defect detection: Sub-band domain co-occurrence matrices. Image and Vision Computing Vol. 18, Nos. 6–7, 543–553, 2000.CrossRefGoogle Scholar
  18. [18]
    Dai, D.; Riemenschneider, H.; Van Gool, L. The synthesizability of texture examples. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3027–3034, 2014.Google Scholar
  19. [19]
    Bridson, R. Fast Poisson disk sampling in arbitrary dimensions. In: Proceedings of the SIGGRAPH Sketches, 22, 2007.Google Scholar
  20. [20]
    Wei, L. Y. Parallel Poisson disk sampling. ACM Transactions on Graphics Vol. 27, No. 3, Article No. 20, 2008.Google Scholar
  21. [21]
    Oliva, A.; Torralba, A. Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision Vol. 42, No. 3, 145–175, 2001.CrossRefzbMATHGoogle Scholar
  22. [22]
    Chang, C.-C.; Lin, C.-J. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology Vol. 2, No. 3, Article No. 27, 2011.Google Scholar
  23. [23]
    Ojala, T.; Pietikinen, M.; Menp, T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 24, No. 7, 971–987, 2000.CrossRefGoogle Scholar
  24. [24]
    Sanchez-Avila, C.; Sanchez-Reillo, R. Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation. Pattern Recognition Vol. 38, No. 2, 231–240, 2005.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Open Access The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from To submit a manuscript, please go to

Authors and Affiliations

  1. 1.Shenzhen UniversityShenzhenChina

Personalised recommendations