Indian Journal of Science and Technology
DOI: 10.17485/ijst/2015/v8i7/62862
Year: 2015, Volume: 8, Issue: 7, Pages: 670–677
Original Article
S. Arumugadevi 1* and V. Seenivasagam2
1 Department of Information Technology, Sri Krishna Engineering College, Chennai, India; sa_devi@yahoo.co.in
2 Department of Computer Science and Engg, National Engineering College (Autonomous), Kovilpatti, India; yespee1094@yahoo.com
Background/Objectives:Imagesegmentationis thefirst stepforanyimageprocessingbasedapplications.TheConventional 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 SelfOrganizing 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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