Object Retrieval by Query with Sensibility Based on the KANSEI-Vocabulary Scale
Abstract
Recently the demand for image retrieval and recognizable extraction corresponding to KANSEI (sensibility) has been increasing, and the studies focused on establishing those KANSEI-based systems have been progressing more than ever. In addition, the attempt to understand, measure and evaluate, and apply KANSEI to situational design or products will be required more and more in the future. Particularly, study of KANSEI-based image retrieval tools have especially been in the spotlight. So many investigators give a trial of using KANSEI for image retrieval. However, the research in this area is still under its primary stage because it is difficult to process higher-level contents as emotion or KANSEI of human. To solve this problem, we suggest the KANSEI-Vocabulary Scale by associating human sensibilities with shapes among visual information. And we construct the object retrieval system for evaluation of KANSEI-Vocabulary Scale by shape. In our evaluation results, we are able to retrieve object images with the most appropriate shape in term of the query’s KANSEI. Furthermore, the method achieves an average rate of 71% user’s satisfaction.
Keywords
Visual Information Image Retrieval Geometrical Form Contour Detection Object RetrievalReferences
- 1.Yamazaki, H., Kondo, K.: A Method of Changing a Color Scheme with KANSEI Scales. Journal for Geometry and Graphics 3(1), 77–84 (1999)zbMATHGoogle Scholar
- 2.Murai, S., Ono, K., Tanaka, N.: KANSEI-based Color Design for CityMap. ARSRIN 2001 1(3) (2001)Google Scholar
- 3.Baek, S., Cho, M., Kim, P.: Matching Colors with KANSEI Vocabulary Using Similarity Measure Based on WordNet. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3480, pp. 37–45. Springer, Heidelberg (2005)CrossRefGoogle Scholar
- 4.Kokubun, M.: System for Visualizing Individual Kansei Information. In: Industrial Electronics Society, IECON 2000, vol. 3, pp. 1592–1597 (2000)Google Scholar
- 5.Kong, H., Kim, W., Oh, K., Kimz, P.: Building the Domain Ontology for Content Based Image Retrieval System. Korea information processing Society, The proceeding of fall conference 9(2) (2002)Google Scholar
- 6.Archaism, R.: Art and visual perception. Mijin Publishing Co. (1995)Google Scholar
- 7.Wong, W.: Principles of Two-Dimentional Design, pp. 5–8. Van Nostrand Reinhold (1972)Google Scholar
- 8.Xu, C., Prince, J.L.: Gradient Vector Flow: A new External Force for Snakes. In: CVPR, Puerto Rico, USA,, pp. 66–71 (1997)Google Scholar
- 9.Xu, C., Prince, J.L.: Snakes, Shapes, and Gradient Vector Flow. IEEE Transactions on Image Processing 7(3), 359–369 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
- 10.
- 11.Latecki, L.J., LakaÈmper, R.: Shape Similarity Measure Based on Correspondence of Visual Parts. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10) (2000)Google Scholar
- 12.Chavez-Aragon, A., Starostenko, O.: Image Retrieval by Ontological Description of Shapes (IRONS), Early Results. In: Proceedings of the First Canadian Conference on Computer and Robot Vision, pp. 341–346 (2004)Google Scholar
- 13.Osgood, C.E., Suci, G.J., Tannenbaum, P.H.: The Measurement of Meaning. Univ. of Illinois Press (1957)Google Scholar