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Concept modeling: From origins to multimedia

Multimedia Tools and Applications volume 51, pages 1175–1200 (2011)Cite this article

Abstract

The origins of concept modeling are in the field of artificial intelligence. This is where the initial algorithms were introduced first. With the emerging developments in the field of multimedia systems, a strong need is generated to examine and implement concepts-based retrieval of multimedia-contents, from large data bases or from the Internet. The early works were based on appropriate modifications of classical approaches. The latest developments utilize the algorithms that make sense only in the case of multimedia systems. This paper presents a number of classical approaches to concept modeling and their applicability to multimedia. Then it discusses a number of approaches introduced specifically for multimedia. Finally it presents an approach which was fully implemented and tested in an academic environment for industry needs.

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References

  1. Babovic Z et al (2010) The media retrieval tool, Proceedings of the VIPSI 2010 AMALFI Conference, Amalfi, Italy, March 2010

  2. Ballan L, Bertini M, Bimbo AD, Serra G (2010) Video annotation and retrieval using ontologies and rule learning. IEEE MultiMedia, Unconditionally accepted, pending publication

  3. Bryant RE (2007) Data-intensive supercomputing: the case for DISC, Technical report, School of Computer Science, Carnegie Mellon University

  4. Chan C (2004) The knowledge modelling system and its application, Canadian Conference on Electrical and Computer Engineering, 2–5 May 2004, pp 1353–1356, Vol. 3

  5. Chein M, Mugnies M (1992) Conceptual graphs: fondamental notions. Rev Intell Artif 6(4):365–406

    Article  Google Scholar 

  6. Chen P (2007) The entity-relationship model—toward a unified view of data, ACM Press, ACM Transactions on Database Systems 1(1):9–36

  7. Chen H, Finin T (2003) An ontology for context aware pervasive computing environments. Cambridge University Press, September 2003, Vol. 18, Issue 03

  8. Chua T, Pung H, Lu G, Jong H (1994) A concept-based image retrieval system, Proceedings of the Twenty-Seventh Hawaii International Conference on System Sciences. Vol. III: Information systems: decision support and knowledge-based systems, Wailea, Hawaii, USA, 4–7 Jan. 1994, pp 590–598

  9. Cook D (2007) SUBDUE graph based knowledge discovery. http://www.uta.edu/ra/real/editprofile.php?pid=176&onlyview=1

  10. Deacon T (1998) The symbolic species – the co-evolution of language and the brain. W.W.Northon & Company, USA

  11. Djordjevic N, Mestrovic N, Mijovic DJ, Dimovski B (2010) Applied concept modeling techniques for semantic data retrieval. IPSI Transaction on Internet Research 6(1):27–30

    Google Scholar 

  12. Diligenti M, Kovacevic M (2010) Visual pagerank: improving the random surfer model using visual features. IPSI Transaction on Internet Research 6(1):18–26

    Google Scholar 

  13. Dou D, McDermott D, Qi P (2004) Ontology translation by ontology merging and automated reasoning. Yale University, New Haven

    Google Scholar 

  14. Frawley W, Piatetsky-Shapiro G, Matheus J (1992) Knowledge discovery in databases: an overview. The American Association for Artificial Intelligence, USA

    Google Scholar 

  15. Fujihara H, Simmons D (1997) Knowledge conceptualization tool. IEEE Trans Knowl Data Eng Archive 9(2):209–220

    Article  Google Scholar 

  16. Gauch S, Madrid J, Induri S, Ravindran D, Chadalavada S (2002) KeyConcept: a conceptual search engine, Information and Telecommunication Technology Center, Technical Report: ITTC-FY2004-TR-8646–37. University of Kansas, USA

    Google Scholar 

  17. Giugno R, Sasha D (2007) GraphGrep. http://alpha.dmi.unict.it/~graphgrep/index.html

  18. Gomez-Perez A, Corcho O (2002) Ontology languages for the semantic web. IEEE Intell Syst 17(1):54–60

    Article  Google Scholar 

  19. Halladay S, Milligan C (2004) The application of network science principles to knowledge simulation, Proceedings of the 37th Annual Hawaii International Conference on System Sciences, Hawaii, 5–8 Jan. 2004

  20. Halpin T (2007) Object role modeling, Neumont University, USA, http://www.orm.net/

  21. Han J, Huang Y, Cercone N, Fu Y (1996) Intelligent query answering by knowledge discovery techniques. IEEE Trans Knowl Data Eng 8(3):373–390

    Article  Google Scholar 

  22. Hawkins J (2007) Learn like a human, IEEE Spectrum on-line, April 2007

  23. Hollink L, Worring M, Schreiber ATH (2005) Building a visual ontology for video retrieval, In Proc. of the ACM Multimedia, pp 479–482, November 2005

  24. Hoogs A, Rittscher J, Stein G, Schmiederer J (2003) Video content annotation using visual analysis and a large semantic knowledgebase, In Proc. of the Conf. on Computer Vision and Pattern Recognition

  25. Hunter J (2001) Adding multimedia to the semantic web—building an mpeg-7 ontology, In International Semantic Web Working Symposium

  26. Jung MY, Park SH (2008) Semantic-based scene retrieval using ontologies for video server, ITC-CSCC-2008, Japan, pp 45–48

  27. Milutinovic V et al (2010) Concept modeling for multimedia contents, Proceedings of the VIPSI 2010 AMALFI Conference, Amalfi, Italy, March 2010

  28. Nakabasami C (2002) An inductive approach to assertional minning for web ontology revision, International Semantic Web Conference (ISWC2002), Sardinia, Italy, 9–12 June 2002

  29. Novak J, Cañas A (2005) The theory of underlying concept maps and how to construct them, Technical ReportFlorida Institute for Human and Machine Cognition CmapTools 2006-01, USA

  30. OWL (2004) Web Ontology Working Group. http://www.w3.org/2004/OWL/

  31. Quinlan JR (1990) Learning logical definitions from relations. Mach Learn 5(3):239–266

    Google Scholar 

  32. Rubin DL, Supekar K, Mongkolwat P, Kleper V, Channin DS (2009) Annotation and image markup: accessing and interoperating with the semantic content in medical imaging. IEEE Intell Syst 24(1):57–65

    Article  Google Scholar 

  33. Salton G, Wong A (1975) A vector space model for automatic indexing. Commun ACM 18(11):613–620

    Article  MATH  Google Scholar 

  34. Siegel et al (1985) Basics of image understanding, Purdue University, An ONR Technical Report

  35. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12)

  36. Sowa J (2000) Ontology, metadata, and semiotics, Proceedings of the Linguistic on Conceptual Structures: Logical Linguistic and Computational Issues table of contents, pp 55–81

  37. Sowa J, Tepfenhart W, Cyre W (1999) Conceptual graphs: draft proposed American National Standard. Springer-Verlag, Berlin, Germany, Lecture Notes in Computer Science, pp 1–65

  38. The concept modeler software package. Stanford University, 2006

  39. The DAML Ontologies (2007) DARPA, USA, www.daml.org/ontologies

  40. The INIS Thesauri (1981) International Atomic Energy Agency (IAEA), Vienna, Austria, January 1981

  41. Unified Modeling Language (2007) Object Management Group. http://www.uml.org/

  42. Varga E, Furlan B, Milutinovic V (2010) Document filter based on extracted concepts. IPSI Transaction on Internet Research 6(1):5–9

    Google Scholar 

  43. Viola P, Jones M (2001) Robust real-time object detection. Technical Report CRL 20001/01, Cambridge Research Laboratory

  44. Voss A, Nakata K, Juhnke M (1999) Concepts as knowledge handles in collaborative document management, International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises, Stanford, USA, 16–18 June 1999, pp 245–252

  45. Vrochidis S, Dulaverakis C, Gounaris A, Nidelkou E, Makris L, Kompatsiaris Y (2008) A hybrid ontology and visual-based retrieval model for cultural heritage multimedia collections. Int J Metadata Semant Ontol 3(3):167–182

    Article  Google Scholar 

  46. Wan X (2009) Combining content and context similarities for image retrieval, Book: Advances in Information Retrieval, Springer Berlin / Heidelberg, pp 749–754

  47. Woods W (1997) Conceptual indexing: a better way to organize knowledge, Sun Microsystems, USA, Technical Report: TR-97-61

  48. Yan R, Fleury MO, Merler M, Natsev A, Smith JR (2009) Large-scale multimedia semantic concept modeling using robust subspace bagging and MapReduce, Proceedings of the First ACM Workshop on Large-scale Multimedia Retrieval and Mining. ACM, New York, pp 35–42

  49. Zellweger P (2003) A knowledge–based model to database retrieval, Proceedings of the International Conference on Integration of Knowledge Intensive Multi-Agent Systems, 30 Sept.–4 Oct. 2003, pp 747–753

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Acknowledgments

The authors would like to thank Charles Milligan of Sun Microsystems, USA, and Gerald O’Nions of StorageTek, France, who initiated their research interest in this exciting field. Also, to Tom Lincoln of the University of Southern California, USA, Roger Shannon of Duke University, USA, and William Robertson of Dalhousie University, Canada, who provided qualified and detailed feedback on this research. Finally, discussions with Nobel Laureates, Martin Perl of Stanford University and Jerome Friedman of MIT, helped shape up the final version of this paper.

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Authors and Affiliations

  1. University of Ljubljana, Ljubljana, Slovenia

    Sanida Omerovic & Sašo Tomazic

  2. University of Belgrade, Belgrade, Serbia

    Zoran Babovic & Veljko Milutinovic

  3. University of Podgorica and Telekom Montenegro, Podgorica, Montenegro

    Zhilbert Tafa

  4. Singidunum University, Belgrade, Serbia

    Veljko Milutinovic

Authors
  1. Sanida Omerovic
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  2. Zoran Babovic
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  3. Zhilbert Tafa
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  4. Veljko Milutinovic
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  5. Sašo Tomazic
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Corresponding author

Correspondence to Zoran Babovic.

Additional information

This research was conceptualized at Purdue University, West Lafayette, Indiana, USA, as a part of the grant # 2588-1314.

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Omerovic, S., Babovic, Z., Tafa, Z. et al. Concept modeling: From origins to multimedia. Multimed Tools Appl 51, 1175–1200 (2011). https://doi.org/10.1007/s11042-010-0642-8

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Keywords

  • Concepts
  • Knowledge
  • Ontology
  • Semantics
  • Multimedia
  • Retrieval
  • Understanding
  • Data
  • Relations
  • Representation