Linking KANSAI and Image Features by Multi-layer Neural Networks

  • Xinyin Huang
  • Shouta Sobue
  • Tomoki Kanda
  • Yen-Wei Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4693)


KANSEI is a Japanese term which means psychological feeling or image of a product. KANSEI engineering refers to the translation of consumers’ psychological feeling about a product into perceptual design elements. Recently several researches have been done for image indexing or image retrieval based on KANSEI factors. In this paper, we report a quantitative study on relationship between image color features and human KNASEI factors. We use the semantic differential (SD) method to extract the KANSEI factors (impressions) such as bright, warm from 4 group subjects (Children, students, adults, elderly person) while they viewing an image (painting). A neural network is used to learn the mapping functions (relationships) from the image feature space to human KANSEI factor space (psychological space).


KANSEI impression image color neural network SD method 


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  1. 1.
    Schwte, S., Eklund, J., Axelsson, J.R.C., Nagamachi, M.: Concepts, methods and tools in Kansei Engineering. Theoretical Issues in Ergonomics Science 5, 5214–5232 (2004)Google Scholar
  2. 2.
    Grimsæth, K.: Linking emotions and product features. KANSEI Engineering, 1–45 (2005)Google Scholar
  3. 3.
    Black, J., Kahol, K., Tripathi, P., Panchanathan, S.: Indexing natural images for retrieval based on Kansei. In: Proc. of Human Vision and Electronic Imaging conference (2004)Google Scholar
  4. 4.
    Takagi, H., Noda, T.: Media Converter with Impression Preservation Using a Neuro-Genetic Approach. International J. of Hybrid Intelligent Systems 1, 49–56 (2004)CrossRefGoogle Scholar
  5. 5.
    Takagi, H., Noda, T., Cho, S.: Psychological Space to Hold Impression Among Media in Common for Media Database Retrieval System. In: SMC’99. Proc. of IEEE International Conference on System, Man, and Cybernetics, Tokyo, Japan, vol. VI, pp. 263–268. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  6. 6.
    Gianfelici, F., Biagetti, G., Crippa, P., e Turchetti, C.: A Novel KLT Algorithm Optimized for Small Signal Sets. In: IEEE ICASSP 2005. Proceedings of 2005 IEEE International Conference of Acoustics, Speech and Signal Processing, pp. 18–23. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  7. 7.
    Sticker, M.A., Orengo, M.: Similarity of color images. In: Proc. SPIE Storage Retrieval Still Image Video Database, pp. 381–392 (1996)Google Scholar
  8. 8.
    Deng, Y., Manjunath, B.S.: An efficient color representation for image retrieval. IEEE Trans. Image Processing 10, 140–147 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Zeng, X.Y., Chen, Y.W., Nakao, Z.: Independent Component Analysis for Color Indexing. IEICE Trans. Information & Systems E87-D, 997–1003 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Xinyin Huang
    • 1
  • Shouta Sobue
    • 2
  • Tomoki Kanda
    • 2
  • Yen-Wei Chen
    • 2
  1. 1.School of Education, Soochow University, Suzhou, Jiangsu 215006China
  2. 2.College of Information Science and Eng., Ritsumeikan Univ., Shiga, 525-8577Japan

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