Table of Contents
ISRN Signal Processing
Volume 2011 (2011), Article ID 393891, 18 pages
Research Article

Self-Organizing Map-Based Color Image Segmentation with k-Means Clustering and Saliency Map

1School of Electronics and Information, Shanghai Dianji University, Shanghai 200240, China
2School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798

Received 24 February 2011; Accepted 24 March 2011

Academic Editors: Y.-S. Chen, M. Faundez-Zanuy, and S. Kwong

Copyright Β© 2011 Dongxiang Chi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Natural image segmentation is an important topic in digital image processing, and it could be solved by clustering methods. We present in this paper an SOM-based k-means method (SOM-K) and a further saliency map-enhanced SOM-K method (SOM-KS). In SOM-K, pixel features of intensity and πΏβˆ—π‘’βˆ—π‘£βˆ— color space are trained with SOM and followed by a k-means method to cluster the prototype vectors, which are filtered with hits map. A variant of the proposed method, SOM-KS, adds a modified saliency map to improve the segmentation performance. Both SOM-K and SOM-KS segment the image with the guidance of an entropy evaluation index. Compared to SOM-K, SOM-KS makes a more precise segmentation in most cases by segmenting an image into a smaller number of regions. At the same time, the salient object of an image stands out, while other minor parts are restrained. The computational load of the proposed methods of SOM-K and SOM-KS are compared to J-image-based segmentation (JSEG) and k-means. Segmentation evaluations of SOM-K and SOM-KS with the entropy index are compared with JSEG and k-means. It is observed that SOM-K and SOM-KS, being an unsupervised method, can achieve better segmentation results with less computational load and no human intervention.