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A novel clustering-based image segmentation via density peaks algorithm with mid-level feature

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Abstract

Image segmentation is an important and fundamental task in computer vision. Its performance is mainly influenced by feature representations and segmentation algorithms. In this paper, we propose a novel clustering-based image segmentation approach which can be called ICDP algorithm. It is able to capture the inherent structure of image and detect the nonspherical clusters. Compared to the other segmentation methods based on clustering, there are several advantages as follows: (1) Integral channel features are used to clearly and comprehensively represent the input image by naturally integrating heterogeneous sources of information; (2) cluster number can be determined directly and cluster centers are able to be identified automatically; (3) hierarchical segmentation is easy to be achieved via ICDP algorithm. The PSNR and MSE are applied to quantitatively evaluate the segmentation performance. Experimental results clearly demonstrate the effectiveness of our novel image segmentation algorithm.

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Notes

  1. http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds.

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Acknowledgments

The authors would like to show sincere thanks to the peer reviewers and the editor who made great contributions to the improvement of this paper. This work was partially supported by the major project of National Natural Science Foundation of China (Grant No. 71331005), the international (regional) cooperation project of National Natural Science Foundation of China (Grant No. 71110107026) and the Grants from National Natural Science Foundation of China (Grant No. 61402429).

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Authors and Affiliations

  1. Key Laboratory of Big Data Mining and Knowledge Management, CAS, Beijing, 100190, China

    Yong Shi, Zhensong Chen, Zhiquan Qi, Fan Meng & Limeng Cui

  2. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, 100190, China

    Zhensong Chen & Fan Meng

  3. Research Center on Fictitious Economy and Data Science, CAS, Beijing, 100190, China

    Yong Shi & Zhiquan Qi

  4. School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, 100190, China

    Limeng Cui

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  1. Yong Shi
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  2. Zhensong Chen
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  3. Zhiquan Qi
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  4. Fan Meng
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  5. Limeng Cui
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Correspondence to Zhiquan Qi.

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Shi, Y., Chen, Z., Qi, Z. et al. A novel clustering-based image segmentation via density peaks algorithm with mid-level feature. Neural Comput & Applic 28 (Suppl 1), 29–39 (2017). https://doi.org/10.1007/s00521-016-2300-1

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  • DOI: https://doi.org/10.1007/s00521-016-2300-1

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