Abstract
An image similarity method based on the fusion of similarity scores of feature similarity ranking lists is proposed. It takes an advantage of combining the similarity value scores of all feature types representing the image content by means of different integration algorithms when computing the image similarity. Three fusion algorithms for the purpose of fusing image feature similarity scores from the feature similarity ranking lists are proposed. Image retrieval experimental results of the evaluation on four general purpose image databases with 4,444 images classified into 150 semantic categories reveal that a proposed method results in the best overall retrieval performance in comparison to the methods employing single feature similarity lists when determining image similarity with an average retrieval precision higher about 15%. Compared to two well-known image retrieval system, SIMPLicity and WBIIS, the proposed method brings an increase of 4% and 27% respectively in average retrieval precision. The proposed method based on multiple criteria thus provides better approximation of the user’s similarity criteria when modeling image similarity.
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.
References
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions in Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Stricker, M., Orengo, M.: Similarity of color images, in Storage and Retrieval for Image and Video Databases. In: Proc. SPIE, vol. 2420, pp. 381–392 (1995)
Stricker, M., Orengo, M.: Similarity of color images. In: Proc. of IS&T and SPIE Storage and Retrieval of Image and Video Databases III, San Jose, CA, USA, pp. 381–392 (1995)
Brandt, S., Laaksonen, J., Oja, E.: Statistical shape features in content-based image retrieval. In: Proc. of 15th Int. Conf. on Pattern Recognition (ICPR 2000), Barcelona, Spain, vol. 2, pp. 1066–1069 (2000)
Corel Corporation, Corel Gallery 3.0 (2000), available http://www3.corel.com/
Stejić, Z., Takama, Y., Hirota, K.: Genetic algorithm-based relevance feedback for image retrieval using Local Similarity Patterns. Information Processing and Management 39(1), 1–23 (2003)
Wang, J.Z., Li, J., Wiederhold, G.: Simplicity: Semantics-sensitive Integrated Matching for Picture Libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)
Massachusetts Institute of Technology, Media Lab, Vision Texture Database (2001), available ftp://whitechapel.media.mit.edu/pub/VisTex/
Laaksonen, J., Oja, E., Koskela, M., Brandt, S.: Analyzing low-level visual features using content-based image retrieval. In: Proc. 7th Int. Conf. on Neural Information Processing (ICONIP 2000), Taejon, Korea, pp. 1333–1338 (2000)
Brodatz, P.: Textures: a photographic album for artists and designers, pp. 40–46. Dover Publications, New York (1966)
Jolion, J.M.: Feature Similarity. In: Lew, M.S. (ed.) Principles of Visual Information Retrieval, pp. 121–143. Springer, London (2001)
Castelli, V., Bergman, L.D.: Digital imagery: fundamentals. In: Castelli, V., Bergman, L.D. (eds.) Image Databases: Search and Retrieval of Digital Imagery, pp. 1–10. Wiley, New York (2002)
Veltkamp, R.C., Tanase, M.: Content-Based Image Retrieval Systems: A Survey Technical Report UU-CS-2000-34 (2000)
Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans. on Circuits Syst. Video Technol. 8(5), 655–664 (1998)
Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the Web. In: Proceedings of 10th International World Wide Web conference, Hong Kong, pp. 613–622 (2001)
Roberts, F.S.: Discrete mathematical models with applications to social, biological, and environmental problems. Prentice-Hall, Englewood Cliffs
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
Jović, M., Hatakeyama, Y., Dong, F., Hirota, K. (2006). Image Retrieval Based on Similarity Score Fusion from Feature Similarity Ranking Lists. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_54
Download citation
DOI: https://doi.org/10.1007/11881599_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
Online ISBN: 978-3-540-45917-0
eBook Packages: Computer ScienceComputer Science (R0)