基于One-class SVM的噪声图像分割方法

为解决现有无监督图像分割模型对强噪声环境鲁棒性差、无法适应复杂混合噪声的问题，提出了一种基于One-class SVM方法的改进后的噪声鲁棒图像分割模型。首先，基于One-class SVM构建一种数据离群程度检测机制；然后，将离群程度值引入能量泛函，令分割模型可以在多种噪声强度下获得较为准确的图像信息，同时避免现有方法在强噪声环境下，降权机制失效的问题；最后，通过最小化能量函数，驱动分割轮廓向目标边缘演化。在噪声图像分割实验中，当选取不同类型和强度的噪声时，该模型均能得到较为理想的分割结果。在F₁-score评估标准下，该模型比基于局部相关熵的K-means（LCK）模型高0.2~0.3，在强噪声环境下具有更高的稳定性，且在分割收敛时间上仅略大于LCK模型0.1 s左右。实验结果表明，所提模型在未显著增加分割耗时的前提下，对于概率、极值及混合噪声均有着更强的鲁棒性，并且可以分割带有噪声的自然图像。

Novel image segmentation method with noise based on One-class SVM

To deal with poor robustness in strong noise environment, weak adaptability to complex mixed noise that appear in the existing unsupervised image segmentation models, an improved noise-robust image segmentation model based on One-class SVM (Support Vector Machine) method was proposed. Firstly, a data outlier detection mechanism was constructed based on One-class SVM. Secondly, an outlier degree was introduced into the energy function, so that more accurate image information could be obtained by the proposed model under multiple noise intensities and the failure of weight-descend mechanism in strong noise environment was avoided. Finally, the segmentation contour was driven to the target edge by minimizing the energy function. In noise image segmentation experiments, the proposed model could obtain ideal segmentation results with different types and intensities of noise. Under F₁-score metric, the proposed model is 0.2 to 0.3 higher than LCK (Local Correntropy-based K-means) model, and has better stability in strong noise environments. The segmentation convergence time of the proposed model is only slightly longer than that of LCK model by about 0.1 s. Experimental results show that the proposed model is more robust to probabilistic, extreme values and mixed noise without significantly increase of segmentation time, and can segment natural images with noise.