Comparative analysis of the performance of complex texture clustering driven by computational intelligence methods using multiple clustering models
Document Type
Article
Publication Date
9-1-2022
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.
Keywords
Markov, Classification
Divisions
fac_eng
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]
Publication Title
Computational Intelligence and Neuroscience
Volume
2022
Publisher
Hindawi
Publisher Location
ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON, W1T 5HF, ENGLAND