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Effective algorithm for determining the number of clusters and its application in image segmentation

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Abstract

The k-means algorithm is a popular clustering method for image segmentation. However, the main disadvantage of this algorithm is its dependence on the number of initial clusters. In this paper, we present an optimal criterion which can select the best segmentation result with less number of clusters. The optimal criterion overcomes the shortcoming of initialization based on the intra-class and inter-class difference. Eight digital images were employed to verify the segmentation results of the optimal criterion. Simultaneously, we have improved the traditional k-means algorithm to find the initial clustering centers efficiently. Experimental results show that the segmented images selected by the optimal criterion have sufficient stability and robustness. In addition, we verify the consistency of results by two kinds of objective assessment measures. The proposed optimal criterion can successfully display the best segmentation results precisely and efficiently so as to instead of artificial selection.

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Acknowledgements

This work was partly supported by National Natural Science Foundation of China (71271125, 61502260). We thank the National Natural Science Foundation of China and Natural Science Foundation of Shandong Province for funding. The authors also thank the USC-SIPI image test library in University of Southern California for providing digital images. We are grateful to every researcher who made comments to our work.

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Authors and Affiliations

  1. School of Information, Qilu University of Technology, Jinan, China

    Jialun Pei, Long Zhao & Xiangjun Dong

  2. School of Mathematical Sciences, University of Jinan, Jinan, China

    Xue Dong

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  1. Jialun Pei
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  2. Long Zhao
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Correspondence to Xiangjun Dong.

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Pei, J., Zhao, L., Dong, X. et al. Effective algorithm for determining the number of clusters and its application in image segmentation. Cluster Comput 20, 2845–2854 (2017). https://doi.org/10.1007/s10586-017-1083-1

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  • DOI: https://doi.org/10.1007/s10586-017-1083-1

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