Abstract
From the engineering aspect, the research on Kansei information is a field aimed at processing and understanding how human intelligence processes subjective information or ambiguous sensibility and how such information can be executed by a computer. Our study presents a method of image processing aimed at accurate image retrieval based on human Kansei. We created the Kansei-Vocabulary Scale by associating Kansei of high-level information with shapes among low-level features of an image and constructed the object retrieval system using Kansei-Vocabulary Scale. In the experimental process, we put forward an adaptive method of measuring similarity that is appropriate for Kansei-based image retrieval. We call it “adaptive-Tangent Space Representation (adaptive-TSR)”. The method is based on the improvement of the TSR in 2-dimensional space for Kansei-based retrieval. We then it define an adaptive similarity algorithm and apply to the Kansei-based image retrieval. As a result, we could get more promising results than the existing method in terms of human Kansei.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Yamazaki, H., Kondo, K.: A Method of Changing a Color Scheme with Kansei Scales. Journal for Geometry and Graphics 3(1), 77–84 (1999)
Murai, S., Ono, K., Tanaka, N.: Kansei-based Color Design for City Map. In: ARSRIN 2001, vol. 1(3) (2001)
Bianchi-Berthouze, N.: An Interactive Environment for Kansei Data Mining. In: The Proceeding of Second International Workshop on Multimedia Data Mining, pp. 58–67 (2001)
Baek, S., Hwang, M., Cho, M., Choi, C., Kim, P.: Object Retrieval by Query with Sensibility based on the Kansei-Vocabulary Scale. In: Huang, T.S., Sebe, N., Lew, M., Pavlović, V., Kölsch, M., Galata, A., Kisačanin, B. (eds.) ECCV 2006 Workshop on HCI. LNCS, vol. 3979, pp. 109–119. Springer, Heidelberg (2006)
Latecki, L.J., Lakämper, R.: Shape Similarity Measure Based on Correspondence of Visual Parts. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10) (2000)
Chávez-Aragón, A., Starostenko, O.: Ontological shape-description, a new method for visual information retrieval. In: Proceedings of the 14th International Conference on Electronics, Communications and Computers (CONIELECOMP 2004) (2004)
Chávez-Aragón, 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)
Kokubun, M.: System for Visualizing Individual Kansei Information. In: Industrial Electronics Society, IECON 2000, vol. 3, pp. 1592–1597 (2000)
Baek, S., Ko, K., Jeong, H., Lee, N., You, S., Kim, P.: The Creation of KANSEI-Vocabulary Scale by Shape. In: Pernaer, P. (ed.) Proceeding of Industrial Conference on Data Mining, ibai, pp. 258–268 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hwang, M. et al. (2006). Adaptive-Tangent Space Representation for Image Retrieval Based on Kansei. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_79
Download citation
DOI: https://doi.org/10.1007/11925231_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
eBook Packages: Computer ScienceComputer Science (R0)