Bayesian clustering on images with factor graphs in reduced normal form

Bayesian clustering implemented on a small Factor Graph is utilized in this work to perform associative recall and pattern recognition on images. The network is trained using a maximum likelihood algorithm on images from a standard data set. The two-class labels are fused with the image data into a unique hidden variable. Performances are evaluated in terms of Kullback-Leibler (KL) divergence between forward and backward messages for images and labels. These experiments reveal the nature of the representation that the learning algorithm builds in the hidden variable.