Advertisement

Multimedia Tools and Applications

, Volume 72, Issue 1, pp 667–685 | Cite as

Relevance feedback based on n-tuplewise comparison and the ELECTRE methodology and an application in content-based image retrieval

  • Pawel RotterEmail author
Article

Abstract

In this article we propose a method for information retrieval based on relational Multi-Criteria Decision Making. We assume that a user cannot define precise search criteria so that these criteria must be found based on the user’s assessment of several sample alternatives (‘alternatives’ here are database records, e.g. images). This situation is common in Content-based Image Retrieval, where it is easier for a user to indicate relevant images than to describe a proper query, especially in formal language. The proposed algorithm for the elicitation of criteria is based on ELECTRE III—a method originally designed for ranking a set of alternatives according to defined criteria. In our algorithm, however, the direction of reasoning is reversed: we start with several sample alternatives that have been assigned a rank by the user and then we select criteria that are compatible (in the sense of ELECTRE methodology) with the user’s preferences expressed on a sample set. Then, having determined the user’s criteria, we apply classical ELECTRE III to retrieve the relevant solutions from the database. We implemented the method in Matlab and tested it on the Microsoft Cambridge Image Database.

Keywords

Multiple criteria analysis Relational MCDM ELECTRE III Preference elicitation Content-based Image Retrieval 

Notes

Acknowledgments

This work was supported by the Polish Ministry of Science and Higher Education under SIMPOZ project, no. 0128/R/t00/2010/12. We thank to our colleagues who participated in experiments and to anonymous reviewers for many valuable comments and suggestions.

References

  1. 1.
    El Sayad I, Martinet J, Urruty T, Djeraba C (2012) Toward a higher-level visual representation for content-based image retrieval. Multimed Tool Appl 60:455–482CrossRefGoogle Scholar
  2. 2.
    Everingham M, Gool LV, Williams CKI, Winn J, Zisserman A (2010) The Pascal Visual Object Classes (VOC) Challenge. Int J Comput Vis 88(2):303–338CrossRefGoogle Scholar
  3. 3.
    Figueira J, Mousseau V, Roy B (2005) ELECTRE methods. In: Figueira J, Greco S, Ehrgott M (eds) Multiple criteria decision analysis: State of the art surveys. Springer, Boston, pp 133–162Google Scholar
  4. 4.
    Heesch D, Rüger S (2007) Interaction models and relevance feedback in image retrieval. In: Zhang Y-J (ed) Semantic-based visual information retrieval. IRM Press, Hershey, London, pp 160–186Google Scholar
  5. 5.
    Lew MS (2001) Principles of visual information retrieval. Springer, LondonCrossRefzbMATHGoogle Scholar
  6. 6.
    Lew MS, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval—state of art and challenges. ACM Trans Multimed Comput Commun Appl 2(1):1–19CrossRefGoogle Scholar
  7. 7.
    Li HF, Wang JJ (2007) An improved ranking method for ELECTRE III. In: Zhou H (ed) International Conference on Wireless Communication, Networking and Mobile Computing (WiCOM ′07), Shanghai. pp 6659–6662. doi: 10.1109/WICOM.2007.1634
  8. 8.
    Lian Z, Godil A, Bustos B, Daoudi M, Hermans J (2013) A comparison of methods for non-rigid 3D shape retrieval. Pattern Recogn 46(1):449–461CrossRefGoogle Scholar
  9. 9.
    Mousseau V, Slowinski R (1998) Inferring an ELECTRE TRI Model from assignment examples. J Glob Optim 12(2):157–174CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Mousseau V, Slowinski R, Zielniewicz P (2000) A user-oriented implementation of the ELECTRE-TRI method integrating preference elicitation support. Comput Oper Res 26(7–8):757–777CrossRefGoogle Scholar
  11. 11.
    Muneesawang P, Guan L (2006) Multimedia database retrieval: A human-centered approach. Springer Series on Signals and Communication Technology. SpringerGoogle Scholar
  12. 12.
    Rotter P (2012) Multimedia information retrieval based on pairwise comparison and its application to visual search. Multimed Tool Appl 60(3):573–587. doi: 10.1007/s11042-011-0828-8 CrossRefGoogle Scholar
  13. 13.
    Rotter P, Skulimowski AMJ (2008) A new approach to interactive visual search with RBF networks based on preference modelling. In: Rutkowski L et al. (eds) LNAI 5097. Springer-Verlag Berlin Heidelberg, pp 861–873Google Scholar
  14. 14.
    Rotter P, Skulimowski AMJ (2009) Preference extraction in image retrieval. In: Ma Z (ed) Artificial intelligence for maximizing content based image retrieval. Idea Group Inc, Harshley, pp 235–260CrossRefGoogle Scholar
  15. 15.
    Roy B, Vanderpooten D (1997) An overview on The European school of MCDA: emergence, basic features and current works. Eur J Oper Res 99(1):26–27CrossRefzbMATHGoogle Scholar
  16. 16.
    Schroff F, Criminisi A, Zissermann A (2011) Harvesting image databases from the web. IEEE Trans Pattern Anal Mach Intell 33(4):754–766CrossRefGoogle Scholar
  17. 17.
    Shen X, Wang Z, Cho S-Y (2008) Mining user hidden semantics from image content for image retrieval. J Vis Commun Image Represent 19(3):145–164CrossRefGoogle Scholar
  18. 18.
    Skulimowski AMJ (1996) Decision support systems based on reference sets. AGH Publishers, KrakowGoogle Scholar
  19. 19.
    Skulimowski AMJ (2011) Freedom of choice and creativity in multicriteria decision making. Lect Notes Comput Sci 6746:190–203CrossRefGoogle Scholar
  20. 20.
    Smeets D, Fabry T, Hermans J, Vandermeulen D, Suetens P (2010) Inelastic deformation invariant modal representation for non-rigid 3D object recognition. In: Perales FJ, Fisher RB (eds) VI Conference on Articulated Motion and Deformable Objects (AMDO), Springer, Andratx, Mallorca, Spain. pp 162–171Google Scholar
  21. 21.
    Tao D, Xu D, Li X (eds) (2009) Semantic mining technologies for multimedia databases. IGI Global, Hershey, LondonGoogle Scholar
  22. 22.
    Tian Y, Jiang S, Huang T, Gao W (2009) Semantic classification and annotation of images. In: Tao D, Xu D, Li X (eds) Semantic mining technologies for multimedia databases. IGI Global, Hershey, London, pp 350–377Google Scholar
  23. 23.
    Vasconcelos N (2007) From pixels to semantic spaces:advances in content-based image retrieval. Computer 40(7):20–26CrossRefGoogle Scholar
  24. 24.
    Vogel J, Schiele B (2006) Performance evaluation and optimization for content-based image retrieval. Pattern Recogn 39:897–909CrossRefzbMATHGoogle Scholar
  25. 25.
    Wong WT, Hsu SH (2006) Application of SVM and ANN for image retrieval. Eur J Oper Res 173:938–950CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

Personalised recommendations