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Neural Computing and Applications

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18.04.2016 | Original Article

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

verfasst von: Yong Shi, Zhensong Chen, Zhiquan Qi, Fan Meng, Limeng Cui

Erschienen in: Neural Computing and Applications | Sonderheft 1/2017

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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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Titel: A novel clustering-based image segmentation via density peaks algorithm with mid-level feature
verfasst von: Yong Shi
Zhensong Chen
Zhiquan Qi
Fan Meng
Limeng Cui
Publikationsdatum: 18.04.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe Sonderheft 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2300-1

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