A Hierarchical Model to Support Kansei Mining Process

  • Tomofumi Hayashi
  • Akio Sato
  • Nadia Berthouze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)


Image retrieval by subjective content has been recently addressed by the Kansei engineering community in Japan. Such information retrieval systems aim to include subjective aspects of the users in the querying criteria. While many techniques have been proposed in modeling such users’ aspects, little attention has been placed on analyzing the amount of information involved in this modeling process and the multi-interpretation of such information. We propose a data warehouse as a support for the mining of the multimedia user feedback. A unique characteristic of our data warehouse lays in its ability to manage multiple hierarchical descriptions of images. Such characteristic is necessary to allow the mining of such data, not only at different levels of abstraction, but also according to multiple interpretation of their content. The proposed data warehouse has been used to support the adaptation of web-based image retrieval systems by impression words.


Image Retrieval Data Warehouse User Feedback Subspace Cluster Subjective Impression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Inder, R., Bianchi-Berthouze, N., Kato, T.: K-DIME: A Software Framework for Kansei Filtering of Internet Material. Proceedings of IEEE International Conference on Systems, Man and Cybernetics, Vol. 6, 241–246, Japan (1999).Google Scholar
  2. 2.
    Hattori, R., Fujiyoshi, M., Iida, M.: An Education System on WWW for Study Color Impression of Art Paintings Applied NetCatalog. IEEE International Conference on Systems Man and Cybernetics’ 99, Vol. 6, 218–223, Japan (1999).Google Scholar
  3. 3.
    Lee, S., Harada, A.: A Design Approach by Objective and Subjective Evaluation of Kansei Information Proceedings of International Workshop on Robot and Human Communication, IEEE Press, pp. 327–332, Hamamatsu, Japan, 1998.Google Scholar
  4. 4.
    Rui, Y., T. S. Huang, M. Ortega, and S. Mehrotra.: Relevance Feeback: A power tool in interactive content-based image retrieval. IEEE Transaction on Circuits and Systems for Video Technology, 8(5):644–655, 1998.CrossRefGoogle Scholar
  5. 5.
    Bianchi-Berthouze, N.: Mining Multimedia Subjective Feedback. International Journal of Information Systems, Kluwer Academic Plublishers, 2002Google Scholar
  6. 6.
    Bianchi-Berthouze, N. and L. Berthouze.: Exploring Kansei in Multimedia Information. International Journal on Kansei Engineering, 2(1):1–10, 2001.Google Scholar
  7. 7.
    Pashler, H.: Attention and Visual Perception: Analysing Divided Attention. S.M Kosslyn, D.N. Osherson editors, International Journal of Visual Cognition, 2:71–100, MIT Press, 1996.Google Scholar
  8. 8.
    Bianchi-Berthouze, N. and C. Lisetti.: Modeling Multimodal Expression of Users’s Affective Subjective Experience. Fiorella De Rosis Editors, International Journal on User Modeling and User-Adapted Interaction: Special Issue on User Modeling and Adaptation in Affective Computing, 12(1):49–84, 2002.Google Scholar
  9. 9.
    Agrawal, R., Gerhrke, J., Gunopulos, D., Raghavan, P.: Automatic Subspace Clustering of High Dimensionla Data for Data Mining Applications. Proceedings of ACM SIGMOD International Conference on Management of Data, Seattle, Washington, 1998.Google Scholar
  10. 10.
    Kobayashi, S.: Colorist: a practical handbook for personal and professional use. Kodansha Press, 1998Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Tomofumi Hayashi
    • 1
  • Akio Sato
    • 1
  • Nadia Berthouze
    • 1
  1. 1.Database Systems LabUniversity of AizuAizu WakamatsuJapan

Personalised recommendations