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

A novel clustering-based image segmentation via density peaks algorithm with mid-level feature
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一种新颖的基于聚类的图像分割,通过具有中级特征的密度峰值算法

DOI:
10.1007/s00521-016-2300-1
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发表时间:
2017-12
影响因子:
6
通讯作者:
Limeng Cui
Limeng Cui
中科院分区:
计算机科学3区
文献类型:
--
作者:
Yong Shi;Zhensong Chen;Zhiquan Qi;Fan Meng;Limeng Cui

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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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