融合SVM和快速均值漂移的图像分割算法

Image Segmentation Algorithm Based on SVM and Fast Mean Shift

图像分割(Image Segmentation)是图像处理研究领域的一个重要问题,是图像分析、特征提取、模式识别等的基础和关键步骤.快速均值漂移图像分割算法(FMS,Fast Mean Shift)虽然能获得良好的分割结果,但是在处理过程中需要人为设定带宽值,而带宽的设定会对分割结果造成一定程度的影响.针对这个问题,提出一种融合支持向量机(Support Vector Machine,SVM)和FMS的图像分割算法(FMS-SVM),先用FMS模型预分割图像,然后用SVM分类器对像素分类,以改善带宽设定产生的影响.实验结果表明,提出的基于SVM和FMS的图像分割算法可以获得良好的分割结果.

Image segmentation is a challenging problem in the domain of image processing, and it is the foundation and critical step of image analysis, feature extraction, pattern recognition, etc. Although fast mean shift（FMS ） image segmentation algorithm can obtain the good segmentation results, the segmentation process needs setting bandwidth value, which will affect the segmentation result to a certain degree. This paper proposes an image segmentation algorithm based on Support Vector Machine （ SVM ） and FMS, namely FMS- SVM. First,the FMS model is used for pre-segment of an image,and then SVM classifier is applied to classify all pixels. In so doing, the effect of iteration bandwidth on segmentation result can be decreased. The Experimental results show that the proposed FMS-SVM method can obtain good segmentation results.