Kernel-based Algorithm with Weighted Spatial Information Intuitionistic Fuzzy C-means

ZHANG Jieyu; LI Zuoyong

doi:10.11999/JEIT161317

Volume 39 Issue 9

Sep. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2162-2168

ZHANG Jieyu, LI Zuoyong. Kernel-based Algorithm with Weighted Spatial Information Intuitionistic Fuzzy C-means[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2162-2168. doi: 10.11999/JEIT161317

Citation:

ZHANG Jieyu, LI Zuoyong. Kernel-based Algorithm with Weighted Spatial Information Intuitionistic Fuzzy C-means[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2162-2168. doi: 10.11999/JEIT161317

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Kernel-based Algorithm with Weighted Spatial Information Intuitionistic Fuzzy C-means

doi: 10.11999/JEIT161317 cstr: 32379.14.JEIT161317

ZHANG Jieyu¹,
LI Zuoyong²

1.
(School of Science, China Pharmaceutical University, Nanjing 211198, China)
2.

Funds:

The National Natural Science Foundation of China (61501522), Fuzhou Science and Technology Planning Project (2016-S-116), The Program for New Century Excellent Talents in Fujian Province University (NCETFJ), The Key Project of College Youth Natural Science Foundation of Fujian Province (JZ160467), The Fujian Provincial Leading Project (2017H0030)

Received Date: 2016-12-08
Rev Recd Date: 2017-04-18
Publish Date: 2017-09-19

Abstract

Abstract

To overcome the shortcoming of Intuitionistic Fuzzy C-Means (IFCM) that it does not take into account the spatial information, a new Kernel-based algorithm with Weighted Spatial Information (KWSI_IFCM) is proposed. Firstly, the constraint of weighted spatial neighborhood information is added. Secondly, instead of Euclidean distance, kernel-induced function is used to measure the distance between pixels and cluster centers. Thirdly, a new clustering objective function is created and then the iterative expressions of new membership and clustering centers are obtained by optimizing the new function. The quantitative analysis of image segmentation results using the new algorithm, other similar methods and a binarization method based on salient transition region shows that the new algorithm can get the F-measure value with 0.9776. The experimental results demonstrate that the proposed algorithm can obtain higher stability and segmentation accuracy than similar fuzzy C-mean algorithm.
- Fuzzy C-Means clustering,
- Intuitionistic fuzzy set,
- Spatial neighborhood information constraints,
- Kernel space

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References(28)

References

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