@article{4641,
  author = {Peiying Li,  Zhongtang Huo},
  title = {Enhanced Image Segmentation in Computer Vision Using PSOOptimization},
  journal = {Journal of Multimedia Processing and Technologies},
  year = {2026},
  volume = {17},
  number = {1},
  doi = {https://doi.org/10.6025/jmpt/2026/17/1/11-20},
  url = {https://www.dline.info/jmpt/fulltext/v17n1/jmptv17n1_2.pdf},
  abstract = {This paper proposes an improved image segmentation model that combines the K-means clustering algorithm
with Particle Swarm Optimization (PSO) to enhance computer vision performance. Traditional K-means suffers
from sensitivity to initial cluster centers and high computational complexity, especially in RGB color
space. To address these issues, the authors integrate PSO to perform a global search for optimal initial cluster
centers before applying K-means for local refinement. This hybrid approach avoids local optima, reduces
computational load, and accelerates processing speed. Experiments compare the proposed model against
standard K-means and PSO-K models using three color images. Results show the enhanced model achieves
the shortest segmentation runtime across all test images and delivers superior edge detail and segmentation
accuracy. The study highlights that while image complexity affects processing time, the proposed method
maintains consistently high efficiency and precision. The authors conclude that their model significantly
improves image segmentation in computer vision tasks, making it more suitable for real world applications
in fields like agriculture, healthcare, and smart systems. By leveraging color space conversion and intelligent
optimization, the model demonstrates robustness, faster convergence, and better handling of fine image
details compared to existing techniques.},
}