Abstract
In this paper, we apply the algorithms to facilitate learning to kansei modeling and experimentally investigate constructed kansei model itself. We introduce using a vector space as a scheme of the mental representation and place still images in the perceptual space by generating perceptual features. Furthermore we propose a method to manipulate the perceptual data by optimizing modeling parameters based on the kansei scale. After this adaptation we compare the similarity between the kansei clusters using their distance in the space to evaluate if the adapting perceptual space is appropriate for one’s kansei. We have conducted preliminary experiments utilizing image data of TV commercials and briefly evaluated the mental space constructed by our method through the kansei questionnaire.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
B. Indurkha: Metaphor and Cognition. Volume 13 Studies in Cognitive Systems, Kluwer Academic Publishers (1992)
R. Orihara et al.: Improvement of Perception through Task Executions. Proceedings of the 17th Workshop on Machine Intelligence, pp.70–73, (2000)
T. Murakami et al.: Friendly information retrieval through adaptive restructuring of information space. In Proc. of AIEIEA 2000, (2000)
T. Murakami and R. Orihara.: Friendly information retrieval through adaptive restructuring of information space. New Generation Computing, Vol.18, No.2, pp.137–146, (2000)
T. Kato, S. Hirai: Human Media Technology — Human centred Approach to Information Infrastructure —. Proceedings of 1st International Symposium on Digital Libraries, (1995)
T. Kato: Multimedia Database with Visual Interaction Facilities. Proceedings of International Symposium on Cooperative Database Systems for Advanced Applications, (1996)
T. Shibata, T. Kato: General Model of Subjective Interpretation for Street Landscape Image. DEXA, pp.501–510, (1998)
T. Kato: Cognitive user interface to cyber space database: human media technology for global information infrastructure. in Proceedings of the International Symposium on Cooperative Database Systems for Advanced Applications, pp.184–190, (1996)
Quinlan, J.R.: Learning Efficient Classification Procedures and Their Application to Chess End Games. Machine Learning:An Artificial Intelligence Approach, pp.463–482, (1983)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA (1993)
S. Muggleton: Inverse entailment and Progol. New Generation Computing, Vol.13, pp.245–286, (1995)
R. Michalski: A theory and methodology of inductive learning. In R.S. Michalski, J.G. Carbonell,and T.M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, Vol.I, Morgan Kaufmann, Palo Alto, (1983)
T. Murakami et al.: Model Construction Suitable for Learning through Adapting Parameters. Proceedings of the 15th Annual Conference of Japanese Society for Artificial Intelligence, in Japanese, pp.70–73, (2000)
H. Yamazaki and K. Kondo. A method of changing a color scheme with kansei scales. In Proceedings of eighth International Conference on Engineering Computer Graphics and Descriptive Geometry, pp.210–214, (1998)
M. Ikeada: Foundation of Color Engineering. Asakura Shoten, in Japanese, (1980)
P. Heckbert.: Color Image Quantization for Frame Buffer Display, Computer Graphics, Vol.16, No.3, (1982)
Y. Morohara et al: Automatic picking of index colors in textile pictures. Proceedings of the 6th International Conference on Engineering Computer Graphics and Descriptive Geometry, pp.643–647, (1994)
H. Liu and H. Motoda: Feature Extraction, Construction, and Selection. Kluwer Academic Publishers, (1998)
Devijver, P.A. and Kittler, J.: Pattern Recognition:A Statistical Approach. Prentice Hall, (1982)
Almuallim, H. and Dietterich, T.G.: Learning With Many Irrelevant Features. Proceedings of the Ninth National Conference on Artificial Intelligence, pp.547–552, (1991)
K. Kira and L.A. Rendell. The Feature Selection Problem:Traditional Methods and New Algorithm. Proceedings of the National Conference on Artificial Intelligence, American Association for Artificial Intelligence, pp.129–134, (1992).
L.A. Rendell and H. Cho. The Effect of Data Character on Empirical Concept Learning. Proceedings of the National Conference on Artificial Intelligence, American Association for Artificial Intelligence, pp.199–205, (1989)
C.E. Osgood, G.J. Suci, and P.H. Tannenbaum: The Measurement of Meaning. University Illinois, Urbana, (1957)
S. Tsutsui et al.: Multi-parent Recombination with Simplex Crossover in Real Coded Genetic Algorithms. Proceedings of Genetic and Evolutionary Computation Conference, pp.657–664, (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Murakami, T., Orihara, R., Sueda, N. (2001). Specification of Kansei Patterns in an Adaptive Perceptual Space. In: Stumptner, M., Corbett, D., Brooks, M. (eds) AI 2001: Advances in Artificial Intelligence. AI 2001. Lecture Notes in Computer Science(), vol 2256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45656-2_31
Download citation
DOI: https://doi.org/10.1007/3-540-45656-2_31
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42960-9
Online ISBN: 978-3-540-45656-8
eBook Packages: Springer Book Archive