Construct Connotation Dictionary of Visual Symbols

Conference paper

Abstract

We present the first version of an electronic dictionary(http://vis.upf.edu/CDVS/dic2.aspx) where designers can find pictures to represent abstract concepts. It aims at the expressiveness and variety of visual expressions for abstract concepts. This dictionary is driven by an automatic knowledge extraction method, which elicits pairs of abstract concept and picture from corpus. The extracted visual symbols look promising. A preliminary experiment was accomplished to test the quality and quantity of these visual symbols. We offer analysis of the experiment results and proposals to improve the knowledge extraction method.

Keywords

visual symbol abstract concept connotation design knowledge extraction corpus clustering data mining survey user experiment work efficiency 

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Notes

Acknowledgements

This work is supported by the FI-IQUC grant from Agència de Gesti’o d’Ajuts Universitaris I de Recerca, Catalunya, Spain. I also would like to thank the discussion and support from Rodrigo Roman and Fabien Girardin.

References

  1. 1.
    Google Image Search, http://images.google.com/.
  2. 2.
    Mougenot C., Bouchard C., Aoussat A., Fostering innovation in early design stage: a study of inspirational process in car design companies, Wonderground 2006 in proc. of the Design Research Society International conference, Portugal 2006.Google Scholar
  3. 3.
    Getty Images, www.gettyimages.com.
  4. 4.
  5. 5.
    Popular categories in iStockphoto, http://www.istockphoto.com/popular.php.
  6. 6.
    Feng Jing , Changhu Wang , Yuhuan Yao , Kefeng Deng , Lei Zhang , Wei-Ying Ma, IGroup: web image search results clustering, Proceedings of the 14th annual ACM international conference on Multimedia, October 23-27, 2006, Santa Barbara, CA, USA.Google Scholar
  7. 7.
    P.-A. Mo¨ellic, J.-E. Haugeard, and G. Pitel. Image clustering based on a shared nearest neighbors approach for tagged collections. In CIVR ’08: Proceedings of the 2008 international conference onContent-based image and video retrieval, pages 269–278, New York, NY, USA, 2008. ACM.CrossRefGoogle Scholar
  8. 8.
    Fellbaum, Christiane, editor. 1998. WordNet:An Electronic Lexical Database. MIT Press, Cambridge, Massachusetts.MATHGoogle Scholar
  9. 9.
    Liu Y., Zhang D., Lu G., Ma W.Y., A survey of content-based image retrieval with highlevel semantics, Pattern Recognition, 40 (2007), pp 262-282.MATHCrossRefGoogle Scholar
  10. 10.
    G. Salton. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, 1989.Google Scholar
  11. 11.
    F. Beil, M. Ester, and X. Xu. Frequent term-based text clustering. In Proc. 8th Int. Conf. on Knowledge Discovery and Data Mining (KDD)’2002, Edmonton, Alberta, Canada, 2002.Google Scholar
  12. 12.
    Zhao, Y. & Karypis, G. (2001). Criterion functions for document clustering: Experiments and analysis. Technical Report TR #01–40, Department of Computer Science, University of Minnesota, Minneapolis, MN.Google Scholar
  13. 13.
    Y Zhao and G Karypis. 2005. Hierarchical clustering algorithms for document data sets. Data Mining and Knowledge Discovery, 10(2):141.168.CrossRefMathSciNetGoogle Scholar
  14. 14.
  15. 15.
  16. 16.
    Toglia MP, Battig WF(1978): Handbook of Semantic Word Norms. Hillsdale, NJ: Erlbaum.Google Scholar

Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  1. 1.Dept. Tecnologies de la Infornació i les ComunicacionsUniversitat Pompeu FabraBarcelonaSpain

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