Rotated Image Based Photomosaic Using Combination of Principal Component Hashing

  • Hideaki Uchiyama
  • Hideo Saito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

This paper introduces a new method of Photomosaic. In this method, we propose to use tiled images that can be rotated in a restricted range. The tiled images are selected from a database. The selection of an image is done by a hashing method based on principal component analysis of a database. After computing the principal components of the database, various kinds of hash tables based on the linear combination of the principal component are prepared beforehand. Using our hashing method, we can reduce the computation time for selecting the tiled images based on the approximated nearest neighbor searching in consideration of a distribution of data in a database. We demonstrate the effectiveness of our hashing method by using a huge number of data in high dimensional space and better looking results of our tiling in experimental results.

References

  1. 1.
    Hausner, A.: Simulating decorative mosaics. In: Proc. ACM SIGGRAPH, pp. 573–580 (2001)Google Scholar
  2. 2.
    Elber, G., Wolberg, G.: Rendering traditional mosaics. The Visual Computer 19, 67–78 (2003)CrossRefGoogle Scholar
  3. 3.
    Blasi, G.D., Petralia, M.: Fast photomosaic. In: Proc. ACM Winter School on Computer Graphics (2005)Google Scholar
  4. 4.
  5. 5.
  6. 6.
    Klein, A.W., et al.: Video mosaics. In: Proc. NPAR, pp. 21–28 (2002)Google Scholar
  7. 7.
    Kim, J., Pellacini, F.: Jigsaw image mosaics. In: Proc. ACM SIGGRAPH, pp. 657–664 (2002)Google Scholar
  8. 8.
    Di Blasi, G., Gallo, G., Petralia, M.: Puzzle image mosaic. In: Proc. VIIP (2005)Google Scholar
  9. 9.
    Arya, S., et al.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. Journal of the ACM 45, 891–923 (1998)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Proc. CVPR, pp. 2161–2168 (2006)Google Scholar
  11. 11.
    Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proc. VLDB, pp. 518–529 (1999)Google Scholar
  12. 12.
    Datar, M., et al.: Locality-sensitive hashing scheme based on p-stable distributions. In: Proc. SCG, pp. 253–262 (2004)Google Scholar
  13. 13.
    Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Communications of the ACM 51, 117–122 (2008)CrossRefGoogle Scholar
  14. 14.
    Matsushita, Y., Wada, T.: Principal component hashing for general distributions. In: Proc. IPSJ SIG Technical Report, 283–288 (2008) (in Japanese)Google Scholar
  15. 15.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. PAMI 20, 1254–1259 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hideaki Uchiyama
    • 1
  • Hideo Saito
    • 1
  1. 1.Keio UniversityKohoku-kuJapan

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