Optimization of Quadtree Representation and Compression

  • Xiang Yin
  • Ryszard Janicki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7413)

Abstract

The quadtrees are a popular representation method for spatial data. In 2009, a heuristic algorithm, called CORN (Choosing an Optimal Root Node), for finding a root node of a region quadtree, has been proposed. It substantially reduces the number of leaf nodes when compared with the standard quadtree decomposition. In this paper, some approximation ideas are applied to improve the CORN algorithm. The empirical results indicate that the new proposed algorithm improves the quadtree representation and data compression.

Keywords

Root Node Leaf Node Binary Image Outer Approximation White Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Finkel, R., Bentley, J.: Quad trees: A data structure for retrieval on composite keys. Acta Informatica 4, 1–9 (1974)MATHCrossRefGoogle Scholar
  2. 2.
    Gautier, N.K., Iyengar, S.S., Lakhani, N.B., Manohar, M.: Space and time efficiency of the forest-of-quadtrees representation. Image and Vision Computing 3(2), 63–70 (1985)CrossRefGoogle Scholar
  3. 3.
    Horowitz, E., Sahni, S.: Fundamentals of Data Structures. Computer Science Press, Potomac (1976)MATHGoogle Scholar
  4. 4.
    Janicki, R.: Approximations of Arbitrary Binary Relations by Partial Orders: Classical and Rough Set Models. In: Peters, J.F., Skowron, A., Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) Transactions on Rough Sets XIII. LNCS, vol. 6499, pp. 17–38. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Pawlak, Z.: Rough Sets. Kluwer, Dordrecht (1991)MATHCrossRefGoogle Scholar
  6. 6.
    Ranade, S., Rosenfeld, A., Samet, H.: Shape approximation using quadtrees. Pattern Recognition 15(1), 31–40 (1982)CrossRefGoogle Scholar
  7. 7.
    Samet, H.: Data Structures for Quadtree Approximation and Compression. Image Processing and Computer Vision 28(9), 973–993 (1985)Google Scholar
  8. 8.
    Yin, X., Düntsch, I., Gediga, G.: Choosing the root node of a quadtree. In: Proc. of GrC 2009 (4th IEEE Intern. Conf. on Granular Computing), Nanchang, China, pp. 721–726 (2009)Google Scholar
  9. 9.
    Yang, Y.H., Chuang, K.L., Tsai, Y.H.: A compact improved quadtree representation with image manipulations. Image and Vision Computing 19(3), 223–231 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiang Yin
    • 1
  • Ryszard Janicki
    • 1
  1. 1.Department of Computing and SoftwareMcMaster UniversityHamiltonCanada

Personalised recommendations