Zhou, Jincheng and Wang, Dan and Ling, Lei and Li, Mingjiang and Lai, Khin-Wee (2022) Comparative analysis of the performance of complex texture clustering driven by computational intelligence methods using multiple clustering models. Computational Intelligence and Neuroscience, 2022. ISSN 1687-5265, DOI https://doi.org/10.1155/2022/8449491.
Full text not available from this repository.Abstract
Traditional texture cluster algorithms are frequently used in engineering; however, despite their widespread application, these algorithms continue to suffer from drawbacks including excessive complexity and limited universality. This study will focus primarily on the analysis of the performance of a number of different texture clustering algorithms. In addition, the performance of traditional texture classification algorithms will be compared in terms of image size, clustering number, running time, and accuracy. Finally, the performance boundaries of various algorithms will be determined in order to determine where future improvements to these algorithms should be concentrated. In the experiment, some traditional clustering algorithms are used as comparative tools for performance analysis. The qualitative and quantitative data both show that there is a significant difference in performance between the different algorithms. It is only possible to achieve better performance by selecting the appropriate algorithm based on the characteristics of the texture image.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) [61862051], Science and Technology Foundation of Guizhou Province [[2019]1299], Top-notch Talent Program of Guizhou province [[2018] 080], Natural Science Foundation of Education of Guizhou province [[2019]203], Qiannan Normal University for Nationalities [qnsy2018003] [qnsy2019rc09] [qnsy2018JS013] |
| Uncontrolled Keywords: | Markov; Classification |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General) > Medical technology T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Aug 2023 04:41 |
| Last Modified: | 30 Aug 2023 04:41 |
| URI: | http://eprints.um.edu.my/id/eprint/41082 |
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