Autonomously Communicating Conceptual Knowledge Through Visual Art

Aus de_evolutionary_art_org
Version vom 12. November 2015, 23:53 Uhr von Gubachelier (Diskussion | Beiträge)

(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu: Navigation, Suche


Reference

Derrall Heath, David Norton and Dan Ventura: Autonomously Communicating Conceptual Knowledge Through Visual Art. In: Computational Creativity 2013 ICCC 2013, 97-104.

DOI

Abstract

In visual art, the communication of meaning or intent is an important part of eliciting an aesthetic experience in the viewer. Building on previous work, we present three ad- ditions to DARCI that enhances its ability to communicate concepts through the images it creates. The first addition is a model of semantic memory based on word associations for providing meaning to concepts. The second addition com- poses universal icons into a single image and renders the im- age to match an associated adjective. The third addition is a similarity metric that maintains recognizability while allow- ing for the introduction of artistic elements. We use an online survey to show that the system is successful at creating im- ages that communicate concepts to human viewers.

Extended Abstract

Bibtex

@inproceedings{
author = {Derrall Heath, David Norton and Dan Ventura},
title = {Autonomously Communicating Conceptual Knowledge Through Visual Art},
editor = {Simon Colton, Dan Ventura, Nada Lavrac, Michael Cook},
booktitle = {Proceedings of the Fourth International Conference on Computational Creativity},
series = {ICCC2013},
year = {2013},
month = {Jun},
location = {Sydney, New South Wales, Australia},
pages = {97-104},
url = {http://www.computationalcreativity.net/iccc2013/download/iccc2013-heath-norton-ventura.pdf, http://de.evo-art.org/index.php?title=Autonomously_Communicating_Conceptual_Knowledge_Through_Visual_Art },
publisher = {International Association for Computational Creativity},
keywords = {computational, creativity},
}

Used References

Bay, H.; Ess, A.; Tuytelaars, T.; and Gool, L. V. 2008. Speeded-up robust features (SURF). Computer Vision and Image Understanding 110:346–359.

Burgess, C. 1998. From simple associations to the building blocks of language: Modeling meaning in memory with the HAL model. Behavior Research Methods, Instruments, & Computers 30:188–198.

Colton, S. 2011. The Painting Fool: Stories from building an automated painter. In McCormack, J., and d’Inverno, M., eds., Computers and Creativity. Springer-Verlag.

Cs ́ıkzentmih ́alyi, M., and Robinson, R. E. 1990. The Art of Seeing. The J. Paul Getty Trust Office of Publications.

Csurka, G.; Dance, C. R.; Fan, L.; Willamowski, J.; and Bray, C. 2004. Visual categorization with bags of keypoints. In Proceedings of the Workshop on Statistical Learning in Computer Vision, 1–22.

De Deyne, S., and Storms, G. 2008. Word associations: Norms for 1,424 Dutch words in a continuous task. Behavior Research Methods 40(1):198–205.

Deerwester, S.; Dumais, S. T.; Furnas, G. W.; Landauer, T. K.; and Harshman, R. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science 41(6):391–407.

Denoyer, L., and Gallinari, P. 2006. The Wikipedia XML corpus. In INEX Workshop Pre-Proceedings, 367–372.

Elkan, C. 2003. Using the triangle inequality to acceler- ate k-means. In Proceedings of the Twentieth International Conference on Machine Learning, 147–153.

Erk, K. 2010. What is word meaning, really?: (and how can distributional models help us describe it?). In Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics, 17–26. Stroudsburg, PA, USA: Asso- ciation for Computational Linguistics.

Fellbaum, C., ed. 1998. WordNet: An Electronic Lexical Database. The MIT Press.

Heath, D.; Norton, D.; and Ventura, D. 2013. Conveying semantics through visual metaphor. ACM Transactions of Intelligent Systems and Technology, to appear.

Kiss, G. R.; Armstrong, C.; Milroy, R.; and Piper, J. 1973. An associative thesaurus of English and its computer analy- sis. In Aitkin, A. J.; Bailey, R. W.; and Hamilton-Smith, N., eds., The Computer and Literary Studies. Edinburgh, UK: University Press.

Krzeczkowska, A.; El-Hage, J.; Colton, S.; and Clark, S. 2010. Automated collage generation — with intent. In Pro- ceedings of the 1st International Conference on Computa- tional Creativity, 36–40.

Likert, R. 1932. A technique for the measurement of atti- tudes. Archives of Psychology 22(140):1–55.

Lund, K., and Burgess, C. 1996. Producing high- dimensional semantic spaces from lexical co-occurrence. Behavior Research Methods, Instruments, & Computers 28:203–208.

McCorduck, P. 1991. AARON’s Code: Meta-Art, Artificial Intelligence, and the Work of Harold Cohen. W. H. Freeman & Co.

Nelson, D. L.; McEvoy, C. L.; and Schreiber, T. A. 1998. The University of South Florida word association, rhyme, and word fragment norms. http://www.usf.edu/FreeAssociation/.

Norton, D.; Heath, D.; and Ventura, D. 2010. Establishing appreciation in a creative system. In Proceedings of the 1st International Conference on Computational Creativity, 26– 35.

Norton, D.; Heath, D.; and Ventura, D. 2011. Autonomously creating quality images. In Proceedings of the 2nd Interna- tional Conference on Computational Creativity, 10–15.

Norton, D.; Heath, D.; and Ventura, D. 2013. Finding cre- ativity in an artificial artist. Journal of Creative Behavior, to appear.

Sivic, J.; Russell, B. C.; Efros, A. A.; Zisserman, A.; and Freeman, W. T. 2005. Discovering objects and their location in images. International Journal of Computer Vision 1:370– 377.

Sun, R. 2008. The Cambridge Handbook of Computational Psychology. New York, NY, USA: Cambridge University Press, 1st edition.

Thomas, S.; Boatman, E.; Polyakov, S.; Mumenthaler, J.; and Wolff, C. 2013. The noun project. http://thenounproject.com.

Turney, P. D., and Pantel, P. 2010. From frequency to mean- ing: Vector space models of semantics. Journal of Artificial Intelligence Research 37:141–188.

Wandmacher, T.; Ovchinnikova, E.; and Alexandrov, T. 2008. Does latent semantic analysis reflect human associ- ations? In Proceedings of the ESSLLI Workshop on Distri- butional Lexical Semantics, 63–70.


Links

Full Text

http://www.computationalcreativity.net/iccc2013/download/iccc2013-heath-norton-ventura.pdf

intern file

Sonstige Links