Pieczynski W, Bouvrais J, Michel C
IEEE Trans Image Process. 2000;9(2):308-12. doi: 10.1109/83.821750.
This paper deals with unsupervised Bayesian classification of multidimensional data. We propose an extension of a previous method of generalized mixture estimation to the correlated sensors case. The method proposed is valid in the independent data case, as well as in the hidden Markov chain or field model case, with known applications in signal processing, particularly speech or image processing. The efficiency of the method proposed is shown via some simulations concerning hidden Markov fields, with application to unsupervised image segmentation.
本文探讨多维数据的无监督贝叶斯分类。我们提出将先前的广义混合估计方法扩展到相关传感器的情况。所提出的方法在独立数据情况下有效，在隐马尔可夫链或场模型情况下也有效，在信号处理，特别是语音或图像处理中有已知的应用。通过一些关于隐马尔可夫场的模拟展示了所提方法的效率，并将其应用于无监督图像分割。
