Advertisement

Kite Recognition by Means of Graph Matching

  • Kamel Madi
  • Hamida Seba
  • Hamamache Kheddouci
  • Charles-Edmont Bichot
  • Olivier Barge
  • Christine Chataigner
  • Remy Crassard
  • Emmanuelle Reganon
  • Emmanuelle Vila
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9069)

Abstract

Kites are remnants of long stone walls that outline the shape of a child’s kite. But the kites are huge, their big size makes them often clearly visible on high-resolution satellite images. Identified at first in the Near East, their area of distribution is getting larger and larger. This wide distribution gives new dimensions in the interpretation of these structures. Consequently, a large scale recognition of kites will help archeologists to understand the functionality of these enigmatic constructions. In this paper, we investigate how the satellite imagery can be exploited in this purpose using a graph representation of the kites. We propose a similarity measure and a kite identification process that can highlights the preservation state of the kites. We also construct from real images a benchmark of kite graphs that can be used by other researchers.

Keywords

Kite recognition graph matching edit distance satellite image 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bunke, H., Riesen, K.: Recent advances in graph-based pattern recognition with applications in document analysis. Pattern Recognition 44, 1057–1067 (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    Chung, Y.C., Han, T., He, Z.: Building recognition using sketch-based representations and spectral graph matching. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2014–2020 (September 2009)Google Scholar
  3. 3.
    Desolneux, A., Moisan, L., Morel, J.M.: Meaningful alignments. Int. J. Comput. Vision 40(1), 7–23 (2000)CrossRefzbMATHGoogle Scholar
  4. 4.
    Échallier, J.C., Braemer, F.: Nature and function of ’desert kites’: new datta and hypothesis. Paléorient 21(1), 35–63 (1995)CrossRefGoogle Scholar
  5. 5.
    Foggia, P., Percannella, G., Vento, M.: Graph matching and learning in pattern recognition in the last 10 years. International Journal of Pattern Recognition and Artificial Intelligence 28(01), 1450001 (2014)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Gao, X., Xiao, B., Tao, D., Li., X.: A survey of graph edit distance. Pattern Analysis Applications (13), 113–129 (2010)Google Scholar
  7. 7.
    von Gioi, R., Jakubowicz, J., Morel, J.M., Randall, G.: Lsd: A fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(4), 722–732 (2010)CrossRefGoogle Scholar
  8. 8.
    Helms, S., Betts, A.V.G.: The desert ’kites’ of the badiyat esh-sham and north arabia. Paléorient 13(1), 41–67 (1987)CrossRefGoogle Scholar
  9. 9.
    Illingworth, J., Kittler, J.: A survey of the hough transform. Computer Vision, Graphics, and Image Processing 44(1), 87–116 (1988)CrossRefGoogle Scholar
  10. 10.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Research Logistics Quarterly 2, 83–97 (1955)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Lacoste, C., Descombes, X., Zerubia, J.: Point processes for unsupervised line network extraction in remote sensing. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10), 1568–1579 (2005)CrossRefGoogle Scholar
  12. 12.
    Lopresti, D.P., Wilfong, G.T.: Comparing Semi-Structured Documents via Graph Probing. In: Workshop on Multimedia Information Systems, pp. 41–50 (2001)Google Scholar
  13. 13.
    McKay, B.: Practical graph isomorphism. Congress Numerantium 87, 30–45 (1981)Google Scholar
  14. 14.
    Raveaux, R., Burie, J.C., Ogier, J.M.: A graph matching method and a graph matching distance based on subgraph assignments. Pattern Recognition Letters 31, 394–406 (2010)CrossRefGoogle Scholar
  15. 15.
    Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image and Vision Computing 27, 950–959 (2009)CrossRefGoogle Scholar
  16. 16.
    Rochery, M., Jermyn, I.H., Zerubia, J.: Higher order active contours. Int. J. Comput. Vision 69(1), 27–42 (2006)CrossRefGoogle Scholar
  17. 17.
    Sanfeliu, A., Fu, K.: A distance measure between attributed relational graphs for pattern recognition. IEEE Transactions on Systems, Man, and Cybernetics (Part B) 13(3), 353–363 (1983)CrossRefzbMATHGoogle Scholar
  18. 18.
    Ullmann, J.R.: An Algorithm for Subgraph Isomorphism. J. ACM 23(1), 31–42 (1976), http://dx.doi.org/10.1145/321921.321925 CrossRefMathSciNetGoogle Scholar
  19. 19.
    Vento, M.: A long trip in the charming world of graphs for pattern recognition. Pattern Recognition 48(2), 291–301 (2015)CrossRefGoogle Scholar
  20. 20.
    Wang, Q., Jiang, Z., Yang, J., Zhao, D., Shi, Z.: A hierarchical connection graph algorithm for gable-roof detection in aerial image. IEEE Geoscience and Remote Sensing Letters 8(1), 177–181 (2011)CrossRefGoogle Scholar
  21. 21.
    Xiao, Y., Dong, H., Wu, W., Xiong, M., Wang, W., Shi, B.: Structure-based graph distance measures of high degree of precision. Pattern Recognition 41, 3547–3561 (2008)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kamel Madi
    • 1
  • Hamida Seba
    • 1
  • Hamamache Kheddouci
    • 1
  • Charles-Edmont Bichot
    • 2
  • Olivier Barge
    • 3
  • Christine Chataigner
    • 3
  • Remy Crassard
    • 3
  • Emmanuelle Reganon
    • 3
  • Emmanuelle Vila
    • 3
  1. 1.LIRIS, CNRS, UMR5205Université de LyonLyonFrance
  2. 2.LIRIS, UMR5205Ecole Centrale de LyonLyonFrance
  3. 3.CNRS, UMR 5133 ArchéorientLyonFrance

Personalised recommendations