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http://dx.doi.org/10.3745/KIPSTB.2009.16-B.1.7

A Study of Post-processing Methods of Clustering Algorithm and Classification of the Segmented Regions  

Oh, Jun-Taek (영남대학교 컴퓨터공학과)
Kim, Bo-Ram (영남대학교 컴퓨터공학과)
Kim, Wook-Hyun (영남대학교 전자정보공학부)
Publication Information
The KIPS Transactions:PartB / v.16B, no.1, 2009 , pp. 7-16 More about this Journal
Abstract
Some clustering algorithms have a problem that an image is over-segmented since both the spatial information between the segmented regions is not considered and the number of the clusters is defined in advance. Therefore, they are difficult to be applied to the applicable fields. This paper proposes the new post-processing methods, a reclassification of the inhomogeneous clusters and a region merging using Baysian algorithm, that improve the segmentation results of the clustering algorithms. The inhomogeneous cluster is firstly selected based on variance and between-class distance and it is then reclassified into the other clusters in the reclassification step. This reclassification is repeated until the optimal number determined by the minimum average within-class distance. And the similar regions are merged using Baysian algorithm based on Kullbeck-Leibler distance between the adjacent regions. So we can effectively solve the over-segmentation problem and the result can be applied to the applicable fields. Finally, we design a classification system for the segmented regions to validate the proposed method. The segmented regions are classified by SVM(Support Vector Machine) using the principal colors and the texture information of the segmented regions. In experiment, the proposed method showed the validity for various real-images and was effectively applied to the designed classification system.
Keywords
Clustering Algorithm; Image Segmentation; EWFCM; Region Classification;
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