基于变分高斯混合模型的图像分割算法

提出了一种基于变分推断的高斯混合模型的图像分割算法. 该算法首先用贝叶斯混合高斯模型对图像的特征进行建模, 并针对模型的参数学习问题, 利用变分推断算法估计模型的参数及其后验概率; 这种方法比采样法的计算量更少, 而且能够根据图像数据自动优化混合个数, 实现了模型的自动选择. 最后, 该算法在Berkeley的自然图像集上进行的实验结果与经典的图像分割算法进行了比较, 结果表明此方法得到的图像分割结果精度较高, 具有较好的性能.

Image Segmentation via Variational Mixture of Gaussions

Gaussian mixture model （GMM） has been effectively used in image segmentation. In this case, the features of an image are described by a mixture model with K different components. However, how to choose the number of mixture components K and estimate model parameters are still short of solutions. Current algorithms such as maximum likelihood and sampling methods are known for their own limitations. So we present an alternative algorithm based on Bayesian variational method and apply it in image segmentation. This method works at less computational cost than sampling methods, and can also naturally handle the model selection problem. In the model＇s iterative process, the algorithm can automatically determine the number of mixture components in view of the data collected. By comparing our method against other classical segmentation methods on natural images acquired from Berkeley Segmentation Data Set, it suggests that our method provides better performance on image segmentation.