Automatic Color Image Segmentation Using Spatial Constraint Based Clustering

  • Abu Shama
  • Santanu Phadikar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 298)


Color image segmentation is a much talked about topic in image processing, where there is plenty of scope for improvement. A cluster validation index based novel method for automatic color image segmentation is proposed here. To identify the number of segments automatically cluster validity indices (Partition Coefficient, Partition Entropy, Xie-Beni index, Kwon’s index and Fuzzy hyper-volume index) have been used. Image has been segmented into the number of segments identified by cluster validation indices using modified Fuzzy C-means (FCM) algorithm, which not only uses the color values, but also the spatial relation of the pixels to identify the segment. The performance of the proposed segmentation algorithm has been evaluated using the benchmark data from Berkeley image segmentation dataset and also been compared with existing Otsu’s method, K-means algorithm and FCM algorithms based segmentation method using Jaccard Index (JI). Experimental results show that the proposed method gives better segmentation results both subjective and in terms of JI values.


Color image segmentation Fuzzy C-means clustering Cluster validation index Automatic segmentation 


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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of CSEWest Bengal University of TechnologyKolkataIndia

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