Title: A modified training scheme for SOFM to cluster multispectral images

Authors: T.N. Nagabhushan, D.S. Vinod

Addresses: Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysore, India. ' Department of Information Science and Engineering, Sri Jayachamarajendra College of Engineering, Mysore, India

Abstract: In this paper, we propose modifications to Kohonen|s Self-Organising Feature Map (SOFM) to achieve faster convergence specifically with respect to multispectral images. First, the raw image is pre-processed using data reduction technique to obtain reduced data set and then Condensed Nearest Neighbour (CNN) rule is applied to yield standard subset of samples. The samples in the standard subset are used to find the Best Matching Unit (BMU) and the samples in the reduced data set are used to update BMU and its neighbouring neurons. The SOFM is tested on: synthetic image data set and Harangi 1991, 1992 image data sets. Results are compared with conventional SOFM.

Keywords: SOFM; self-organising feature map; CNN; condensed nearest neighbour; reduced data sets; standard subsets; BMU; best matching unit; multispectral images; image clustering.

DOI: 10.1504/IJSISE.2010.034630

International Journal of Signal and Imaging Systems Engineering, 2010 Vol.3 No.1, pp.31 - 39

Received: 28 Jun 2009
Accepted: 29 Mar 2010
Published online: 13 Aug 2010 *

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