Balanced-Tree Structure Bayesian Networks (TSBNs) for Image Segmentation
In this work, I made a more generalized version of Gaussian mixture model (GMM) by putting prior balanced-tree structure over the class variables (mixture components) in the hope that the induced correlation among the hidden variables would suppress the noise in the resulting segmentation. Unlike supervised image classification by , this work focuses on totally unsupervised segmentation using TSBN. In this work, it is interesting to see how the data will be “self-organized” according to the initial structure given by TSBN.
 X. Feng, C.K.I. Williams, S.N. Felderhof, “Combining Belief Networks and Neural Networks for Scene Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 467-483, April, 2002