Composition Based Semantic Scene Retrieval for Ancient Murals

  • Qi Wang
  • Dongming Lu
  • Hongxin Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)


Retrieval of similar scenes in ancient murals research is an important but time-consuming job for researchers. However, content-based image retrieval (CBIR) systems cannot fully deal with such issues since they lack of the abilities to handle complex semantic and image composition queries. In this paper, we introduce a new semantic scene-retrieval approach for ancient murals. Our method can retrieve related scenes according to both their content elements and their composition through a two-phase procedure. Then, retrieved scenes are ranked according to composition-based criterion that incorporates the relevance of semantic content and visual structures with scene compactness ratio. Hence, the sorted results are tailored to the real intent of query. The experiments demonstrate the efficiency and effectiveness of our approach to reduce the semantic gap of visual information retrieval. Furthermore, the retrieval results for Dunhuang murals suggest the potential applications for general paintings retrieval and personalized publishing.


semantic retrieval ontology query expansion theory of composition ancient murals 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Academy, D.: Chinese Grotto-Dunhuang Grotto. Cultural Relic Publishing House, Beijing (1987) (in Chinese)Google Scholar
  2. 2.
    Heng, F., Chua, T.S.: A boostrapping approach to annotating large image collection. In: Workshop on Multimedia Information Retrieval in ACM Multimedia, pp. 55–620. ACM Press, New York (2003)Google Scholar
  3. 3.
    Hu, T.Q.: An introduction to dunhuang grotto art. Dunhuang Research (3), 16–34 (1993) (in Chinese)Google Scholar
  4. 4.
    Jin, W., Shi, R., Chua, T.S.: A semi-naive bayesian method incorporating clustering with pair-wise constraints for auto image annotation. In: ACM Multimedia, pp. 336–339. ACM Press, New York (2004)Google Scholar
  5. 5.
    Johnson, M.: Semantic Segmentation and Image Search. Phd thesis, University of Cambridge (2008)Google Scholar
  6. 6.
    Leacock, C., Chodorow, M.: Combining local context and wordnet similarity for word sense identification. In: WordNet: A Lexical Reference System and Its Application, pp. 265–283. MIT Press, Cambridge (1998)Google Scholar
  7. 7.
    Li, X., Lu, D.M., Pan, Y.H.: Color restoration and image retrieval for dunhuang fresco preservation. IEEE MultiMedia 7(2), 38–42 (2000)CrossRefGoogle Scholar
  8. 8.
    Li, Y., Bilmes, J., Shapiro, L.: Object class recognition using images of abstract regions. In: International Conference on Pattern Recognition, pp. 40–43. IEEE Press, Washington (2004)Google Scholar
  9. 9.
    Lu, D.M., Pan, Y.H.: Image and semantic feature based dunhuang mural retrieval. Chinese Journal of Computers 21(11), 1022–1026 (1998) (in Chinese)Google Scholar
  10. 10.
    Lu, Y., Hu, C., Zhu, X., Zhang, H., Yang, Q.: A unified framework for semantics and feature based relevance feedback in image retrieval systems. In: ACM Multimedia, pp. 31–37. ACM Press, New York (2000)Google Scholar
  11. 11.
    Mezaris, V., Kompatsiaris, I., Strintzis, M.G.: An ontology approach to object-based image retrieval. In: ICIP, pp. 511–514. IEEE Press, Washington (2003)Google Scholar
  12. 12.
    Natsev, A., Haubold, A., Tešić, J., Xie, L., Yan, R.: Semantic concept-based query expansion and re-ranking for multimedia retrieval. In: The 15th international conference on Multimedia, pp. 991–1000. ACM Press, New York (2007)CrossRefGoogle Scholar
  13. 13.
    Peterson, B.F.: Learning to See Creatively. Amphoto Press, New York (2003)Google Scholar
  14. 14.
    Tatatinov, I., Viglas, S.D., Beyer, K., et al.: Storing and querying ordered xml using a relational database system. In: Proceedings of the 21th ACM SIGMOD International Conference on Management of Data, pp. 204–215. ACM Press, New York (2002)Google Scholar
  15. 15.
    Tsugunari, K., Akira, Y. (tr.).: The Lotus Sutra. Numata Center for Buddhist Translation and Research, 2nd edn., Berkeley, Calif. (2007)Google Scholar
  16. 16.
    Vogel, J., Schiele, B.: Natural scene retrieval based on a semantic modeling step. In: Enser, P.G.B., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 207–215. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Zhang, C., Jiang, J., Pan, Y.: Dunhuang frescoes retrieval based on similarity calculation of color and texture features. In: The IEEE Conference on Information Visualisation, pp. 96–100. IEEE Press, Washington (1997)Google Scholar
  18. 18.
    Zhang, Y.L.: Iconographical study of the two buddhas sitting together at dunhuang, from the northern dynasties to the sui dynasty. Dunhuang Research (4), 24–32 (2009) (in chinese)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Qi Wang
    • 1
  • Dongming Lu
    • 1
    • 2
  • Hongxin Zhang
    • 1
    • 2
  1. 1.College of Computer Science and TechnologyZhejiang University 
  2. 2.State Key Lab of CAD & CGZhejiang UniversityHangzhou, ZhejiangChina

Personalised recommendations