Abstract
The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Deboeck, G., Kohonen, T.: Visual Explorations in Finance with Self-organizing Maps. Springer, London (1998); Japanese ed. Springer, Tokyo (1999)
Kohonen, T.: Self-Organizing Maps, Series in Information Sciences, 2nd edn., vol. 30. Springer, Berlin (1997)
Baba, N., Kojima, F., Ozawa, M.: Basis and Application of Neural Network, Kyoritu Shuppan, Tokyo, Japan (1994)
Kohonen, T., Hynninen, J., Laaksonen, J.: SOM_PAK:The Self-Organizing Map Program Package. Technical Report A31,Helsinki University of Technology, Laboratory of Computer and Information science (1996)
Gudivada, V.N., Raghavan, V.V.(eds.): Content-based Image Retrieval System. IEEE Computer 28(9), 18–22 (1995)
Flickner, M., et al.: Query by Image and Video Content: The QBIC System. IEEE Computer 28(9), 23–32 (1995)
Faloutsos, C., et al.: Efficient and Effective Query by Image Content. J. Intell. Inform. Syst. 3, 231–262 (1994)
Eakins, J.P., Graham, M.E.: Content-based image retrieval. Report to the JISC Technology Applications Programme (1999)
Rui, Y., Huang, T., Chang, S.-F.: Image Retrieval: Current Techniques, Promising Directions, and Open Issues. J. Visual Communication and Image Representation(JVCIR) 10(1), 39–62 (1999)
Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: Proceedings of the ACM SIGMOD Conference, San Jose, CA, May 1995, pp. 71–79 (1995)
Rui, Y., Huang, T.S., Ortega, M., Mehrotra, S.: Relevance Feedback: A Power Tool in Interactive Content-Based Image Retrieval. IEEE Trans. on Circuits and Systems for Video Technology, Special Issue on Segmentation, Description, and Retrieval of Video Content 8(5), 644–655 (1998)
Mallat, S.G.: Multifrequency channel decompositions of images and wavelet models. IEEE. Trans., Acoust.,Speech and Signal Proc. 37(12), 2091–2110 (1989)
Niblack, W., et al.: The QBIC project:Query Image by content using color, texture and shape. In: SPIE Storage and Retrieval for Image and Video Databases, San Joes, pp. 173–187 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Im, DJ. et al. (2005). Similarity Retrieval Based on Self-organizing Maps. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2005. ICCSA 2005. Lecture Notes in Computer Science, vol 3481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424826_50
Download citation
DOI: https://doi.org/10.1007/11424826_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25861-2
Online ISBN: 978-3-540-32044-9
eBook Packages: Computer ScienceComputer Science (R0)