Comparison of clustering methods for segmenting color images

Abstract

Background/Objectives: Image segmentation is the first step for any image processing based applications. The Conventional methods are unable to produce good segmentation results for color images. Methods/Statistical analysis: We present two soft computing approaches namely Fuzzy C-Means (FCM) clustering and Self Organizing Map (SOM) network are used to segment the color images. The segmentation results of FCM and SOM compared to the results of K-Means clustering. Results/Findings: Our experimental results shown that the Fuzzy C-Means and SOM produced the better results than K-means for segmenting complex color images. The time required for the training of SOM is higher. Conclusion/Application: The trained SOM network reduced the execution time for segmenting color images. The performance of FCM and SOM is higher than the K-means for segmenting color images. Applications of color image segmentation are video surveillance, face recognition, fingerprint recognition, object detection, medical image analysis, and Automatic target detection.