基于球向量机的图像分割

摘要

Due to the large scale of the image data,the standard Support Vector Machine(SVM) has a high time complexity in the training process for image segmentation. In this paper,the Ball Vector Machine(BVM) is used for image segmentation in order to reduce the training time. The experimental results show that BVM has a similar segmentation effect and anti-noise performance compared to the standard SVM for image segmentation in the condition of noisy and non-noisy. However, BVM significantly consumes less training time than the standard SVM.BVM can greatly improve the overall performance of image segmentation.%由于图像数据量庞大,将标准支持向量机应用于图像分割时,其训练的时间复杂度较高.通过使用球向量机对图像进行分割,以降低训练过程消耗的时间.实验表明,在无噪声和有噪声情况下,使用球向量机对图像进行分割,其分割效果和抗噪性能与标准支持向量机的分割效果基本相同.然而,球向量机在训练过程中所消耗的时间显著小于标准支持向量机.应用球向量机进行图像分割,可以显著提高图像分割的整体性能.