Research on Clustering Methods for Color Image Segmentation

Authors

  • Qingzhen Gong

DOI:

https://doi.org/10.54097/ajst.v3i3.2551

Keywords:

Color image segmentation, Fuzzy clustering, Fuzzy C-Means, K-Means.

Abstract

The research of image segmentation mainly includes: how to select the appropriate color space, reduce the complexity of segmentation algorithm, improve the noise resistance and universality of segmentation algorithm, etc. Fuzzy clustering is an unsupervised classification method, which can classify samples with similar properties without prior knowledge. This paper briefly describes the working principle and performance comparison of these algorithms.

References

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Rosefeld A, Torre P De La. HSItogram Concavity Analysis as an Aid in Threshold Selection. IEEE Trans. SMC, 1983, 13(2):231-235.

Zhang C, Wang P. A New Method of Color Image Segmentation Based on Intensity and Hue Clustering. Proceedings of 15th International Conference on Pattern Recognition, Barcelona Spain.2011, 3:613-616.

Zhang Yan. A color image segmentation method based on genetic algorithm [J]. Computer Application and Software, 2011, (03).

Udupa J K, Punam K, Saba P K et al. Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation. IEEE Trans on Analysis and Machine Intelligence, 2010, 24(11):1485-1500.

Boskovitz Victor, Guterman Hugo. An Adaptive Neuro-Fuzzy System for Automatic Image Segmentation and Edge Detection. IEEE Transactions on Fuzzy Systems, 2010, 10(2):247-262.

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Published

13 November 2022

How to Cite

Gong, Q. (2022). Research on Clustering Methods for Color Image Segmentation. Academic Journal of Science and Technology, 3(3), 70–72. https://doi.org/10.54097/ajst.v3i3.2551

Issue

Section

Articles