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K-Means, Self Organizing Feature Map, DB-Index, Automatic Splitting and Merging
Image segmentation plays the significant roles in image processing, computer vision and as well as in pattern recognition. The Segmentation process subdivides an image into its constituent parts or objects, such that level of subdivision depends on the problem to be solved. The aim of image segmentation is partitioning an image within homogeneous regions that are significantly meaningful concerning some characteristics like intensity or texture. Based on clustering, a large number of researches have been done in the area of image segmentation. This paper presents an efficient image segmentation method in which the self organizing feature map (SOFM) is used for initial segmentation. After the initial segmentation, the segmented image is used by the K-means algorithm for further segmentation. Finally, the procedures for automatic splitting and merging the cluster are applied to obtain the appropriate number of segments in segmented image and as well as better segmented results. For analyzing the performance, we calculate the statistical measure named as Davies-Bouldin index (DB-index). The observation shows that, this method gives the better segmented results compared with K-Means algorithm, linear discriminant analysis (LDA) and K-Means based image segmentation method and also SOFM and K-Means based image segmentation approach.
Tamanna Yesmin Rashme, Mohammed Nasir Uddin, " Self-Organizing Feature Map and K-Means Algorithm with Automatically Splitting and Merging Clusters based Image Segmentation", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.10, pp. 63-71, 2018. DOI: 10.5815/ijigsp.2018.10.07
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