Color image segmentation using adaptive mean shift and statistical model-based methods
Color image segmentation using adaptive mean shift and statistical model-based methods
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DOI：
10.1016/j.camwa.2008.10.053
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发表时间：
2009-03
期刊：
影响因子：
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通讯作者：
Jonghyun Park;Gueesang Lee;Soon-Young Park
中科院分区：
文献类型：
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作者：
Jonghyun Park;Gueesang Lee;Soon-Young Park
提出了一种基于高斯混合模型的彩色图像无监督分割算法。通过自适应均值漂移自动确定混合分量的个数，通过在特征向量空间中反复搜索密度较高的点来估计局部聚类。对于GMM的参数估计，采用了平均场退火期望最大化(EM)方法。平均场退火法为克服混合模型中的局部极大值问题提供了一个全局最优解。通过将自适应均值漂移和平均场退火法相结合，自动分割自然彩色图像，而不会出现过度分割或孤立区域。实验表明，该算法在不需要任何先验信息的情况下，能够得到满意的分割结果。
In this paper, we propose an unsupervised segmentation algorithm for color images based on Gaussian mixture models (GMMs). The number of mixture components is determined automatically by adaptive mean shift, in which local clusters are estimated by repeatedly searching for higher density points in feature vector space. For the estimation of parameters of GMMs, the mean field annealing expectation-maximization (EM) is employed. The mean field annealing EM provides a global optimal solution to overcome the local maxima problem in a mixture model. By combining the adaptive mean shift and the mean field annealing EM, natural color images are segmented automatically without over-segmentation or isolated regions. The experiments show that the proposed algorithm can produce satisfactory segmentation without any a priori information.