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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Google Image Search, http://images.google.com/.
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.
Getty Images, www.gettyimages.com.
Flickr, http://www.flickr.com/.
Popular categories in iStockphoto, http://www.istockphoto.com/popular.php.
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.
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.
Fellbaum, Christiane, editor. 1998. WordNet:An Electronic Lexical Database. MIT Press, Cambridge, Massachusetts.
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.
G. Salton. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, 1989.
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.
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.
Y Zhao and G Karypis. 2005. Hierarchical clustering algorithms for document data sets. Data Mining and Knowledge Discovery, 10(2):141.168.
Google Analytics, http://www.google.com/analytics/.
Toglia MP, Battig WF(1978): Handbook of Semantic Word Norms. Hillsdale, NJ: Erlbaum.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag US
About this paper
Cite this paper
Xiao, P., Arroyo, E., Blat, J. (2009). Construct Connotation Dictionary of Visual Symbols. In: Huang, M., Nguyen, Q., Zhang, K. (eds) Visual Information Communication. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-0312-9_7
Download citation
DOI: https://doi.org/10.1007/978-1-4419-0312-9_7
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-0311-2
Online ISBN: 978-1-4419-0312-9
eBook Packages: Computer ScienceComputer Science (R0)