Neural Computing and Applications

, Volume 18, Issue 3, pp 223–235 | Cite as

Robust tile-based texture synthesis using artificial immune system

Original Article


One significant problem in tile-based texture synthesis is the presence of conspicuous seams in the tiles. The reason is that sample patches employed as primary patterns of the tile set may not be well stitched if carelessly picked. In this paper, we introduce a robust approach that can stably generate an ω-tile set of high quality and pattern diversity. First, an extendable rule is introduced to increase the number of sample patches to vary the patterns in an ω-tile set. Second, in contrast to other concurrent techniques that randomly choose sample patches for tile construction, ours uses artificial immune system (AIS) to select the feasible patches from the input example. This operation ensures the quality of the whole tile set. Experimental results verify the high quality and efficiency of the proposed algorithm.


Texture synthesis ω-tile Sample patches selection Clonal selection Artificial immune system 



We thank Steve Zelinka, Vivek Kwatra and Tuen-Young Ng for sharing their results and texture samples on the web sites. Thank Professor Ramin Zabih and his students at Cornell University for sharing their code for computing graph min-cut [40]. This work is supported by National Natural Science Foundation of China projects no. 60073007, 60473110; by National High-Tech Research and Development Plan 863 of China under Grant No. 2006AA01Z301, and by the MOST International collaboration project no. 2007DFC10740.


  1. 1.
    Efros AA, Leung TK (1999) Texture synthesis by non-parametric sampling. In: ICCV ’99: Proceedings of the international conference on computer vision, vol 2. IEEE Computer Society, Washington, p 1033Google Scholar
  2. 2.
    Wei LY, Levoy M (2000) Fast texture synthesis using tree-structured vector quantization. In: SIGGRAPH ’00: Proceedings of the 27th annual conference on computer graphics and interactive techniques. ACM Press, New York, pp 479–488Google Scholar
  3. 3.
    Efros AA, Freeman WT (2001) Image quilting for texture synthesis and transfer. In: SIGGRAPH ’01: Proceedings of the 28th annual conference on Computer graphics and interactive techniques. ACM Press, New York, pp 341–346Google Scholar
  4. 4.
    Kwatra V, Schödl A, Essa I, Turk G, Bobick A (2003) Graphcut textures: image and video synthesis using graph cuts. ACM Trans Graph 22:277–286Google Scholar
  5. 5.
    Heeger DJ, Bergen JR (1995) Pyramid-based texture analysis/synthesis. In: SIGGRAPH ’95: Proceedings of the 22nd annual conference on Computer graphics and interactive techniques. ACM Press, New York, pp 229–238Google Scholar
  6. 6.
    Paget R, Longstaff ID (1998) Texture synthesis and unsupervised recognition with a nonparametric multiscale markov random field model. IEEE Trans Image Process 7:925–931Google Scholar
  7. 7.
    Portilla J, Simoncelli EP (2000) A parametric texture model based on joint statistics of complex wavelet coefficients. Int J Comput Vis 40:49–70MATHGoogle Scholar
  8. 8.
    Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22:56–65Google Scholar
  9. 9.
    Kwatra V, Essa I, Bobick A, Kwatra N (2005) Texture optimization for example-based synthesis. ACM Trans Graph 24:795–802Google Scholar
  10. 10.
    Ng TY, Wen C, Tan TS, Zhang X, Kim YJ (2005) Generating an ω-tile set for texture synthesis. In: Proceedings of computer graphics international 2005 (CGI’05), Stone Brook, NY, USA, pp 177–184Google Scholar
  11. 11.
    Cohen MF, Shade J, Hiller S, Deussen O (2003) Wang tiles for image and texture generation. ACM Trans Graph 22:287–294Google Scholar
  12. 12.
    Wei LY (2004) Tile-based texture mapping on graphics hardware. In: HWWS ’04: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on graphics hardware. ACM Press, New York, pp 55–63Google Scholar
  13. 13.
    Dong W, Sun S, Paul JC (2005) Optimal sample patches selection for tile-based texture synthesis. In: CAD-CG ’05: Proceedings of the 9th international conference on computer aided design and computer graphics (CAD-CG’05). IEEE Computer Society, Washington, pp 503–508Google Scholar
  14. 14.
    Dong W, Zhou N, Paul JC (2007) Optimized tile-based texture synthesis. In: GI ’07: Proceedings of graphics interface 2007. ACM, New York, pp 249–256Google Scholar
  15. 15.
    Kim J, Bentley PJ (2001) Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with a negative selection operator. In: Proceedings of the 2001 Congress on evolutionary computation CEC2001. IEEE Press, Seoul, pp 1244–1252Google Scholar
  16. 16.
    de Castro LN, Zuben FJV (2002) Learning and optimization using the clonal selection principle. IEEE Trans Evol Comput 6:239–251Google Scholar
  17. 17.
    Du H, Jiao L, Gong M, Liu R (2004) Adaptive dynamic clone selection algorithms. In: Lecture notes in computer science (RSCTC’2004 proceedings) , vol 3066, pp 768–773Google Scholar
  18. 18.
    Ishida Y (2004) Immunity-based systems: a design perspective. Springer, New YorkGoogle Scholar
  19. 19.
    Bonet JSD (1997) Multiresolution sampling procedure for analysis and synthesis of texture images. In: SIGGRAPH ’97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques. ACM Press, New York, pp 361–368Google Scholar
  20. 20.
    Ashikhmin M (2001) Synthesizing natural textures. In: SI3D ’01: Proceedings of the 2001 symposium on interactive 3D graphics. ACM Press, New York, pp 217–226Google Scholar
  21. 21.
    Wu Q, Yu Y (2004) Feature matching and deformation for texture synthesis. ACM Trans Graph 23:364–367Google Scholar
  22. 22.
    Liu Y, Lin WC, Hays J (2004) Near-regular texture analysis and manipulation. ACM Trans Graph 23:368–376Google Scholar
  23. 23.
    Nealen A, Alexa M (2003) Hybrid texture synthesis. In: EGRW ’03: Proceedings of the 14th Eurographics workshop on rendering, Aire-la-Ville, Eurographics Association, Switzerland, pp 97–105Google Scholar
  24. 24.
    Lefebvre S, Hoppe H (2005) Parallel controllable texture synthesis. ACM Trans Graph 24:777–786Google Scholar
  25. 25.
    Lefebvre S, Hoppe H (2006) Appearance-space texture synthesis. ACM Trans Graph 25:541–548Google Scholar
  26. 26.
    Zelinka S, Garland M (2002) Towards real-time texture synthesis with the jump map. In: EGRW ’02: Proceedings of the 13th Eurographics workshop on Rendering, Aire-la-Ville, Eurographics Association, Switzerland, pp 99–104Google Scholar
  27. 27.
    Zelinka S, Garland M (2004) Jump map-based interactive texture synthesis. ACM Trans Graph 23:930–962Google Scholar
  28. 28.
    Liang L, Liu C, Xu YQ, Guo B, Shum HY (2001) Real-time texture synthesis by patch-based sampling. ACM Trans Graph 20:127–150Google Scholar
  29. 29.
    de Castro LN, Zuben FJV (2000) The clonal selection algorithm with engineering applications. In: Proceedings of GECCO’00: Workshop on Artificial Immune Systems and Their Applications, Las Vegas, Nevada, USA, pp 36–39Google Scholar
  30. 30.
    de Castro LN, Timmis J (2002) Immune systems: a new computational intelligence approach. Springer, BerlinMATHGoogle Scholar
  31. 31.
    de França FO, Zuben FJV, de Castro LN (2005) An artificial immune network for multimodal function optimization on dynamic environments. In: GECCO ’05: Proceedings of the 2005 conference on Genetic and evolutionary computation, ACM Press, New York, pp 289–296Google Scholar
  32. 32.
    Pan Z, Kang L (1997) An adaptive evolutionary algorithm for numerical optimization. In: Simulated evolution and learning: First Asia-Pacific Conf. (SEAL’96), Selected papers, Springer, Berlin, pp 27–34Google Scholar
  33. 33.
    Pérez P, Gangnet M, Blake A (2003) Poisson image editing. ACM Trans Graph 22:313–318Google Scholar
  34. 34.
    Arya S, Mount DM, Netanyahu NS, Silverman R, Wu AY (1998) An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J ACM 45:891–923MATHMathSciNetGoogle Scholar
  35. 35.
    Gersho A, Gray RM (1991) Vector quantization and signal compression. Kluwer, NorwellGoogle Scholar
  36. 36.
    Kilthau SL, Drew MS, Möller T (2002) Full search content independent block matching based on the fast fourier transform. In: Proceedings of international conference on image processing 2002, vol 1. Vancouver, BC, Canada, pp 669–672Google Scholar
  37. 37.
    Soler C, Cani MP, Angelidis A (2002) Hierarchical pattern mapping. ACM Trans Graph 21:673–680Google Scholar
  38. 38.
    Dellaert F, Kwatra V, Oh SM (2005) Mixture trees for modeling and fast conditional sampling with applications in vision and graphics. In: CVPR ’05: Proceedings of the 2005 IEEE Computer Society conference on computer vision and pattern recognition (CVPR’05) , vol 1. IEEE Computer Society, Washington, pp 619–624Google Scholar
  39. 39.
    Nicoll A, Meseth J, Müller G, Klein R (2005) Fractional fourier texture masks: guiding near-regular texture synthesis. Comput Graph Forum 24:569–579Google Scholar
  40. 40.
    Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23:1222–1239Google Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.LIAMA-NLPRCAS Institute of AutomationBeijingChina
  2. 2.Project ALICEINRIA Lorraine/LoriaNancyFrance
  3. 3.Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  4. 4.INRIA/Tsinghua UniversityBeijingChina

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