Abstract
Recently, we have proposed an image indexing and retrieval technique that is based on vector quantization. We have already shown that this technique is more effective than the traditional colour-based techniques. Some factors that must be decided during the implementation of the proposed techniques are codebook size, codeword dimension and colour space. In this paper, we investigate how these factors may affect the retrieval performances of the proposed technique.
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
Y. Gong, H. Zhang and C. Chuan, “An image database system with fast image indexing capability based on colour histograms”, Proceedings of IEEE 10’s Ninth Annual International Conference, Singapore, 22-26 August 1994, pp.407–411.
S. K. Chan, Content-based Image Retrieval, MSc thesis, National University of Singapore, 1994.
M. J. Swain and D. H. Ballard, “Color indexing”, Int. J. Comput. Vision, 7:11–32. 1991.
G. D. Finlayson, Colour Object Recognition, MSc Thesis, Simon Fraser University, 1992.
W. Niblack et al, “QBIC Project: querying images by content, using colour, texture, and shape” Proceedings of Conference on Storage and Retrieval for Image and Video Databases, 1-3 Feb. 1993, San Jose, California, US, SPIE Vol. 1908, pp.1908–1920.
V.D. Lecce and A. Guerriero, “An elvalution of the effectiveness of image features for image retrieval”, Journal of Visual Communication and Image Representation 10, 1999, pp. 351–362.
G. Lu and S. Teng, “A Novel Image Retrieval Technique based on Vector Quantization”, Computational S. Intelligence for Modeling Control and Automation, February 1999, Australia, pp.36–41.
S. Teng and G. Lu, “Performance study of image retrieval based on vector quantization”, ICCIMADE’01: International Conference on Intelligent Multimedia and Distance Education Conference, 1-3 June 2001, Fargo, ND, USA.
S. Teng and G. Lu, “An evaluation of the robustness of image retrieval based on vector quantization”, IEEE Pacific-Rim Conference on Multimedia 2001, October 24-26, 2001, Beijing, China.
K. Sayood, Introduction to Data Compression, Morgan Kaufmann Publishers, Inc., San Francisco, California, 1996.
A. Gersho and R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publishers, 1992.
H. Abut (ed.), Vector Quantization, IEEE Press, 1990.
S. Teng and G. Lu, “Codebook generation in vector quantization used for image retrieval”, International Symposium on Intelligent Multimedia and Distance Education, 2-7 August 1999, Baden-Baden, Germany.
Sangwine S. J. and Horne R. E. N., The colour image processing handbook, Chapman & Hall, London, UK, 1998.
G. Salton, Introduction to Mordern Information Retrieval, McGraw-Hill Book Company, 1983.
G. Lu and A. Sajjanhar, “On performance measurement of multimedia information retrieval systems”, International Conference on Computational Intelligence and Multimedia Applications, 9-11 Feb. 1998, Monash University, pp.781–787.
G. Lu, Multimedia Database Management Systems, Artech House, Boston, US, 1999.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Teng, S., Lu, G. (2002). Effects of Codebook Sizes, Codeword Dimensions, and Colour Spaces on Retrieval Performance of Image Retrieval Using Vector Quantization. In: Chen, YC., Chang, LW., Hsu, CT. (eds) Advances in Multimedia Information Processing — PCM 2002. PCM 2002. Lecture Notes in Computer Science, vol 2532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36228-2_28
Download citation
DOI: https://doi.org/10.1007/3-540-36228-2_28
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00262-8
Online ISBN: 978-3-540-36228-9
eBook Packages: Springer Book Archive