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Attributed Relational SIFT-Based Regions Graph for Art Painting Retrieval

  • Mario Manzo
  • Alfredo Petrosino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

Recently, image retrieval and analysis algorithms have been extensively applied to art related domains. In this field, state-of-the-art approaches mainly focus on feature extraction with the aim of improving reliability of authentication, classification and retrieval of art paintings. In this paper we propose an effective modeling, based on a graph structure, and a retrieval strategy, based on a graph matching algorithm, for art paintings. The proposed approach has been tested on different datasets with high quality results allowing an user to run effective content-based queries on painting records.

Keywords

CBIR systems graph matching graph based image representation local invariant features extraction 

References

  1. 1.
    Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: Fast Retina Keypoint. In: CVPR, pp. 510–517 (2012)Google Scholar
  2. 2.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: Binary Robust Independent Elementary Features. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Carneiro, G.: Graph-based methods for the automatic annotation and retrieval of art prints. In: ICMR, p. 32 (2011)Google Scholar
  5. 5.
    Chang, S.F., Sikora, T., Puri, A.: Overview of the mpeg-7 standard. Circuits and Systems for Video Technology 11(6), 688–695 (2001)CrossRefGoogle Scholar
  6. 6.
    Chatzichristofis, S.A., Boutalis, Y.S.: CEDD: Color and Edge Directivity Descriptor. A Compact Descriptor for Image Indexing and Retrieval. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 312–322. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Chatzichristofis, S.A., Boutalis, Y.S.: FCTH: Fuzzy Color And Texture Histogram A Low Level Feature For Accurate Image Retrieval. In: 9th International Workshop on Image Analysis for Multimedia Interactive Services, pp. 191–196 (2008)Google Scholar
  8. 8.
    Colantoni, P., Jean-Baptiste, T., Ruven, P.: Graph-based 3d visualization of color content in paintings. In: VAST, pp. 25–30 (2010)Google Scholar
  9. 9.
    Duchenne, O., Bach, F., Kweon, I.S., Ponce, J.: A tensor-based algorithm for high-order graph matching. PAMI 33(12), 2383–2395 (2011)CrossRefGoogle Scholar
  10. 10.
    Etezadi-Amoli, M., Chang, C., Hewlett, M.: A day at the museum (2009)Google Scholar
  11. 11.
    Haladová, Z., Šikudová, E.: Limitations of the SIFT/SURF based methods in the classifications of fine art paintings. Computer Graphics and Geometry 12(1), 40–50 (2010)Google Scholar
  12. 12.
    Huang, J., Kumar, S.R., Mitra, M., Zhu, W.J., Zabih, R.: Image indexing using color correlograms. In: CVPR, pp. 762–768 (1997)Google Scholar
  13. 13.
    Koffka, K.: Principles of Gestalt Psychology. Harcourt, New York (1935)Google Scholar
  14. 14.
    Lee, J., Cho, M., Lee, K.M.: Hyper-graph matching via reweighted random walks. In: CVPR, pp. 1633–1640 (2011)Google Scholar
  15. 15.
    Cho, M., Lee, J., Lee, K.M.: Reweighted random walks for graph matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 492–505. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  17. 17.
    Lux, M., Chatzichristofis, S.A.: Lire: Lucene image retrieval: an extensible Java CBIR library. In: 16th ACM Multimedia, pp. 1085–1088 (2008)Google Scholar
  18. 18.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  19. 19.
    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.R.: ORB: An efficient alternative to SIFT or SURF. In: ICCV, pp. 2564–2571 (2011)Google Scholar
  20. 20.
    Ruf, B., Kokiopoulou, E., Detyniecki, M.: Mobile museum guide based on fast SIFT recognition. In: Detyniecki, M., Leiner, U., Nürnberger, A. (eds.) AMR 2010. LNCS, vol. 5811, pp. 170–183. Springer, Heidelberg (2010)Google Scholar
  21. 21.
    Sanroma, G., Alquézar Mancho, R., Serratosa, I., Casanelles, F.: Graph matching using SIFT descriptors - An application to pose recovery of a mobile robot. In: 5th International Conference on Computer Vision Theory and Applications, pp. 249–254 (2010)Google Scholar
  22. 22.
    Tamura, H., Mori, S., Yamawak, T.: Textural features corresponding to visual perception. Systems, Man, and Cybernetics 8(6), 460–472 (1978)CrossRefGoogle Scholar
  23. 23.
    Tremeau, A., Colantoni, P.: Regions adjacency graph applied to color image segmentation. Trans. on Image Processing 9(4), 735–744 (2000)CrossRefGoogle Scholar
  24. 24.
    You, S., Neumann, U.: Mobile augmented reality for enhancing e-learning and e-business. In: International Conference on Internet Technology and Applications, pp. 1–4 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mario Manzo
    • 1
  • Alfredo Petrosino
    • 1
  1. 1.Department of Science and TechnologyUniversity of Naples ‘‘Parthenope’’NapoliItaly

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