Abstract
In large content-based image database applications, efficient information retrieval depends heavily on good indexing structures of the extracted features. While indexing techniques for text retrieval are well understood, efficient and robust indexing methodology for image retrieval is still in its infancy. In this paper, we present a non-hierarchical clustering scheme for index generation using the Rival Penalized Competitive Learning (RPCL) algorithm. RPCL is a stochastic heuristic clustering method which provides good cluster center approximation and is computationally efficient. Using synthetic data as well as real data, we demonstrate the recall and precision performance measurement of nearest-neighbor feature retrieval based on the indexing structure generated by RPCL.
This work is supported in part by the RGC Grant #CUHK4176/97E. Portions of this manuscript have been presented in [8].
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
N. Beckmann, H. P. Kriegel, R. Schneider, and B. Seeger. “The R*tree: an efficient and robust access method for points and rectangles”. ACM SIGMOD Record, 19(2):322–331, 1990.
J. L. Bentley. “Multidimensional Binary Search Trees Used for Associative Searching”. Communications of the ACM, 18(9):509–517, 1975.
Tolga Bozkaya and Meral Ozsoyoglu. “Distance-Based Indexing for High-dimensional Metric Spaces”. SIGMOD Record, 26(2):357–368, June 1997.
T. C. Chiueh. “Content-Based Image Indexing”. In Proceedings of the 20th VLDB Conference, pages 582–593, September 1994.
R. A. Finkel and J. L. Bentley. “Quad Trees: A Data Structure for Retrieval on Composite Keys”. Acta Informatica, 4(1):1–9, 1974.
A. Guttman. “R-trees: A Dynamic Index Structure for Spatial Searching”. ACM SIGMOD, 14(2):47–57, June 1984.
Norio Katayama and Shinichi Satoh. “The SR-tree: an index structure for high-dimensional nearest neighbor queries”. SIGMOD Record, 26(2):369–380, June 1997.
I. King, L. Xu, and L.W. Chan. Using rival penalized competitive clustering for feature indexing in Hong Kong’s textile and fashion image database. In Proceedings to the International Joint Conference on Neural Networks (IJCNN‘98), pages 237–240. IEEE Computer Press, May 4–9, 1998.
T. Kohonen. The self-organizing map. Proc. IEEE, 78:1464–1480, 1990.
R. Rosenblatt. Principles of Neurodynamics. Spartan Books, New York, 1959. neural networks.
T. Sellis, N. Roussopoulos, and C. Faloutsos. “The R+-tree: a dynamic index for multidimensional objects”. In Proceedings of the 13th VLDB Conference, pages 507–518, 1987.
Lei Xu, Adam Krzy_zak, and Erkki Oja. Rival penalized competitive learning for clustering analysis, RBF net, and curve detection. IEEE Trans. on Neural Networks, 4(4):636–649, 1993.
P. N. Yianilos. “Data structures and algorithms for nearest neighbor search in general metric spaces”. In Proc. of the 4th Annual ACM-SIAM Symp. on Discrete Algorithms, pages 311–321, 1993.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1999 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
King, I., Lau, TK. (1999). Non-hierarchical Clustering with Rival Penalized Competitive Learning for Information Retrieval. In: Perner, P., Petrou, M. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 1999. Lecture Notes in Computer Science(), vol 1715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48097-8_10
Download citation
DOI: https://doi.org/10.1007/3-540-48097-8_10
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66599-1
Online ISBN: 978-3-540-48097-6
eBook Packages: Springer Book Archive