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Comparison of Clustering Methods for Segmenting Color Images
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.
Keywords
Clustering, FCM, Image Segmentation, K-Means, SOM, Subtractive Clustering.
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