Abstract: According to the common over-segmentation problem of superpixels image segmentation, a new image segmentation algorithm called Mean Shift based Bayesian Region Merging（MS-BRM） based on Mean Shift algorithm and Nonparametric Bayesian Clustering Model（NBCM） is proposed. Firstly, over-segement an image to superpixels using Mean Shift algorithm. Then, a merging criterion based on NBCM and the spatial information of superprixels is proposed, which is used to form the final segment result by merging the superprixels. Experimental results show that MS-BRM algorithm solves the problem of superpixels over-segmentation, and the final segment result of the images reserves many boundary information, which highly matches human vision.

Key words: superprixels, Nonparametric Bayesian Clustering Model（NBCM）, region merging, spatial information

摘要： 针对超像素分割算法中普遍存在的过分割问题，结合Mean Shift算法和非参数贝叶斯聚类模型，提出了一种新的图像分割算法MS-BRM（Mean Shift based Bayesian Region Merging）。首先，利用Mean Shift算法对图像进行超像素分割，然后根据非参数贝叶斯聚类模型，融合超像素的空间信息，提出一种区域合并策略对超像素进行合并，得到了最终的分割结果。实验结果表明，MS-BRM算法改善了超像素的过分割问题，对图像进行分割的结果保留了图像的边界信息，更加符合人类视觉的判断结果。

关键词: 超像素, 非参数贝叶斯聚类模型, 区域合并, 空间信息