East European Conference on Advances in Databases and Information Systems

ADBIS 2015: New Trends in Databases and Information Systems pp 135-144 | Cite as

MLES: Multilayer Exploration Structure for Multimedia Exploration

  • Juraj Moško
  • Jakub Lokoč
  • Tomáš Grošup
  • Přemysl Čech
  • Tomáš Skopal
  • Jan Lánský
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 539)


The traditional content-based retrieval approaches usually use flat querying, where whole multimedia database is searched for a result of some similarity query with a user specified query object. However, there are retrieval scenarios (e.g., multimedia exploration), where users may not have a clear search intents in their minds, they just want to inspect a content of the multimedia collection. In such scenarios, flat querying is not suitable for the first phases of browsing, because it retrieves the most similar objects and does not consider a view on part of a multimedia space from different perspectives. Therefore, we defined a new Multilayer Exploration Structure (MLES), that enables exploration of a multimedia collection in different levels of details. Using the MLES, we formally defined popular exploration operations (zoom-in/out, pan) to enable horizontal and vertical browsing in explored space and we discussed several problems related to the area of multimedia exploration.


Similarity search Multimedia exploration Content-based retrieval Exploration operation Multimedia browsing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Beecks, C., Uysal, M., Driessen, P., Seidl, T.: Content-based exploration of multimedia databases. In: 2013 11th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 59–64, June 2013Google Scholar
  2. 2.
    Beecks, C., Driessen, P., Seidl, T.: Index support for content-based multimedia exploration. In: Proceedings of the International Conference on Multimedia, MM 2010, pp. 999–1002. ACM, New York (2010)Google Scholar
  3. 3.
    Budikova, P., Batko, M., Zezula, P.: Evaluation platform for content-based image retrieval systems. In: Gradmann, S., Borri, F., Meghini, C., Schuldt, H. (eds.) TPDL 2011. LNCS, vol. 6966, pp. 130–142. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  4. 4.
    Chavez, G.E., Figueroa, K., Navarro, G.: Effective proximity retrieval by ordering permutations. IEEE Trans. Pattern Anal. Mach. Intell. 30(9), 1647–1658 (2008)CrossRefGoogle Scholar
  5. 5.
    Chen, C., Gagaudakis, G., Rosin, P.: Similarity-based image browsing (2000)Google Scholar
  6. 6.
    Chen, J.Y., Bouman, C., Dalton, J.: Hierarchical browsing and search of large image databases. IEEE Transact. on Image Processing 9(3), 442–455 (2000)CrossRefGoogle Scholar
  7. 7.
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: an efficient access method for similarity search in metric spaces. In: VLDB 197, pp. 426–435. Morgan Kaufmann Publishers Inc. (1997)Google Scholar
  8. 8.
    Fruchterman, T.M.J., Reingold, E.M.: Graph drawing by force-directed placement. Softw. Pract. Exper. 21(11), 1129–1164 (1991)CrossRefGoogle Scholar
  9. 9.
    Grošup, T., Čech, P., Lokoč, J., Skopal, T.: A web portal for effective multi-model exploration. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds.) MMM 2015, Part II. LNCS, vol. 8936, pp. 315–318. Springer, Heidelberg (2015) Google Scholar
  10. 10.
    Liu, H., Xie, X., Tang, X., Li, Z.W., Ma, W.Y.: Effective browsing of web image search results. In: Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR 2004, pp. 84–90. ACM (2004)Google Scholar
  11. 11.
    Lokoč, J., Grošup, T., Čech, P., Skopal, T.: Towards efficient multimedia exploration using the metric space approach. In: 2014 12th International Workshop on Content-Based Multimedia Indexing (CBMI), pp. 1–4, June 2014Google Scholar
  12. 12.
    Moško, J., Skopal, T., Bartoš, T., Lokoč, J.: Real-time exploration of multimedia collections. In: Wang, H., Sharaf, M.A. (eds.) ADC 2014. LNCS, vol. 8506, pp. 198–205. Springer, Heidelberg (2014) Google Scholar
  13. 13.
    Navarro, G.: Searching in metric spaces by spatial approximation. The VLDB Journal 11(1), 28–46 (2002)CrossRefGoogle Scholar
  14. 14.
    Novak, D., Batko, M., Zezula, P.: Metric index: An efficient and scalable solution for precise and approximate similarity search. Inf. Syst. 36(4), 721–733 (2011)CrossRefGoogle Scholar
  15. 15.
    Patella, M., Ciaccia, P.: Approximate similarity search: A multi-faceted problem. J. of Discrete Algorithms 7(1), 36–48 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Pecenovic, Z., Do, M.N., Vetterli, M., Pu, P.: Integrated browsing and searching of large image collections. In: Laurini, R. (ed.) VISUAL 2000. LNCS, vol. 1929, pp. 279–289. Springer, Heidelberg (2000) CrossRefGoogle Scholar
  17. 17.
    Schaefer, G.: A next generation browsing environment for large image repositories. Multimedia Tools and Applications 47, 105–120 (2010). doi: 10.1007/s11042-009-0409-2 CrossRefGoogle Scholar
  18. 18.
    Skopal, T., Pokorný, J., Snášel, V.: PM-tree: pivoting metric tree for similarity search in multimedia databases. In: Advances in Databases and Information Systems (2004)Google Scholar
  19. 19.
    Strong, G., Gong, M.: Browsing a large collection of community photos based on similarity on GPU. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part II. LNCS, vol. 5359, pp. 390–399. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  20. 20.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Juraj Moško
    • 1
  • Jakub Lokoč
    • 1
  • Tomáš Grošup
    • 1
  • Přemysl Čech
    • 1
  • Tomáš Skopal
    • 1
  • Jan Lánský
    • 2
  1. 1.SIRET Research Group, Department of Software Engineering, Faculty of Mathematics and PhysicsCharles University in PraguePragueCzech Republic
  2. 2.Department of Computer Science and MathematicsUniversity of Finance and AdministrationPragueCzech Republic

Personalised recommendations