A highly robust image segmentation algorithm based on trade-off factors and multidimensional spatial metrics
Image segmentation is an important research direction in computer vision.Clustering algorithms,serving as an unsupervised method,have always been a powerful tool for image segmentation.However,in scenarios where image possess high-intensity noise and complex structures,the segmentation effect of clustering algorithms might prove unsatisfactory.To address this problem,a highly robust image segmentation algorithm was proposed based on trade-off factors and multi-dimensional space metrics.Firstly,a trade-off factor was introduced to effectively reduce the influence of noise on the segmentation result by adjusting the factor.Secondly,the algorithm integrated both low-dimensional and high-dimensional space metrics,enabling the capture of linear and nonlinear features in the image.In this way,the algorithm facilitated a more comprehensive understanding of the complex structure and texture in the image,thereby enhancing the accuracy and robustness of segmentation.Finally,the algorithm achieved image segmentation through the application of an enhanced fuzzy clustering algorithm.To verify the performance of the algorithm,extensive experiments were conducted on synthetic,natural,and medical images,and the results demonstrated that the proposed method significantly outperformed other algorithms in terms of segmentation.
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