Version 1
: Received: 25 June 2024 / Approved: 25 June 2024 / Online: 26 June 2024 (06:34:42 CEST)
How to cite:
Jiang, Y.; Yeh, W.-C.; Lin, Z. A Fast Multi-Threshold Color Image Segmentation Algorithm Using Bayesian Forecasting Evolutionary Algorithm. Preprints2024, 2024061791. https://doi.org/10.20944/preprints202406.1791.v1
Jiang, Y.; Yeh, W.-C.; Lin, Z. A Fast Multi-Threshold Color Image Segmentation Algorithm Using Bayesian Forecasting Evolutionary Algorithm. Preprints 2024, 2024061791. https://doi.org/10.20944/preprints202406.1791.v1
Jiang, Y.; Yeh, W.-C.; Lin, Z. A Fast Multi-Threshold Color Image Segmentation Algorithm Using Bayesian Forecasting Evolutionary Algorithm. Preprints2024, 2024061791. https://doi.org/10.20944/preprints202406.1791.v1
APA Style
Jiang, Y., Yeh, W. C., & Lin, Z. (2024). A Fast Multi-Threshold Color Image Segmentation Algorithm Using Bayesian Forecasting Evolutionary Algorithm. Preprints. https://doi.org/10.20944/preprints202406.1791.v1
Chicago/Turabian Style
Jiang, Y., Wei-chang Yeh and Zhengchun Lin. 2024 "A Fast Multi-Threshold Color Image Segmentation Algorithm Using Bayesian Forecasting Evolutionary Algorithm" Preprints. https://doi.org/10.20944/preprints202406.1791.v1
Abstract
Multi-threshold image segmentation is a relevant research field in further higher-level image preprocessing and computer vision. This paper adopts the Bayesian Forecasting Evolutionary Algorithm (BFEA) for natural scenery image segmentation using multilevel thresholding. An update method for the prediction vector is designed for image segmentation. The design idea of the BFEA algorithm for multilevel thresholding is based on the basic principle of Evolutionary Computation, the probability distribution of promising solutions, and the Bayesian theorem. BFEA could effectively solve the curse of dimensionality. Extensive experiments have demonstrated that the proposed algorithm dominated the state-of-the-art population-based thresholding method in term of the quality of image, function fitness value, optimal threshold value, SSIM value, PSNR value, computation time, dimensionality issue and convergence rate. The proposed method is very effective for color image segmentation.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
