Establishing Appreciation In a Creative System
Inhaltsverzeichnis
Reference
David Norton, Derrall Heath, Dan Ventura: Establishing Appreciation In a Creative System. In: Computational Creativity 2010 ICCC 2010. 26-35.
DOI
Abstract
Colton discusses three conditions for attributing creativity to a system: appreciation, imagination, and skill. We describe an original computer system (called DARCI) that is designed to eventually produce images through creative means. We show that DARCI has already started gaining appreciation, and has even demonstrated imagination, while skill will come later in her development.
Extended Abstract
Bibtex
@inproceedings{ author = {David Norton, Derrall Heath, Dan Ventura}, title = {Establishing Appreciation In a Creative System}, editor = {Dan Ventura, Alison Pease, Rafael P ́erez y P ́erez, Graeme Ritchie and Tony Veale}, booktitle = {Proceedings of the First International Conference on Computational Creativity}, series = {ICCC2010}, year = {2010}, month = {January}, location = {Lisbon, Portugal}, pages = {26-35}, url = {http://computationalcreativity.net/iccc2010/papers/norton-heath-ventura.pdf, http://de.evo-art.org/index.php?title=Establishing_Appreciation_In_a_Creative_System }, publisher = {International Association for Computational Creativity}, keywords = {computational, creativity}, }
Used References
1. DARCI (Digital ARtist Communicating Intention). http://axon.cs.byu.edu/DARCI/.
2. Distributed content-based visual information retrieval system on peer-to-pear(p2p) network. http://appsrv.cse.cuhk.edu.hk/~miplab/discovir/.
3. S. Colton. Creativity versus the perception of creativity in computational systems. Creative Intelligent Systems: Papers from the AAAI Spring Symposium, pages 14–20, 2008.
4. R. Datta, D. Joshi, J. Li, and J. Z. Wang. Studying aesthetics in photographic images using a computational approach. Lecture Notes in Computer Science, 3953:288–301, 2006.
5. C. Fellbaum, editor. WordNet: An Electronic Lexical Database. The MIT Press, 1998.
6. T. Gevers and A. Smeulders. Combining color and shape invariant features for image re- trieval. IEEE Transactions on Image Processing, 9:102–119, 2000.
7. C. Li and T. Chen. Aesthetic visual quality assessment of paintings. IEEE Journal of Selected Topics in Signal Processing, 3:236–252, 2009.
8. X. Shen, M. Boutell, J. Luo, and C. Brown. Multi-label machine learning and its application to semantic scene cassification, 2004.
9. W.-N. Wang and Q. He. A survey on emotional semantic image retrieval. Proceedings of the International Conference on Image Processing, 2008.
10. W.-N. Wang, Y.-L. Yu, and S.-M. Jiang. Image retrieval by emotional semantics: A study of emotional space and feature extraction. IEEE International Conference on Systems, Man, and Cybernetics, 4:3534–3539, 2006.
11. J. Zujovic, L. Gandy, and S. Friedman. Identifying painting genre using neural networks. miscellaneous, 2007.
Links
Full Text
http://computationalcreativity.net/iccc2010/papers/norton-heath-ventura.pdf
http://axon.cs.byu.edu/papers/norton2010iccc.pdf