This paper presents a Bayesian image segmentation model based on Potts prior and loopy belief propagation. The proposed Bayesian model involves several terms, including the pairwise interactions of Potts models, and the average vectors and covariant matrices of Gauss distributions in color image modeling. These terms are often referred to as hyperparameters in statistical machine learning theory. In order to determine these hyperparameters, we propose a new scheme for hyperparameter estimation based on conditional maximization of entropy in the Potts prior. The algorithm is given based on loopy belief propagation. In addition, we compare our conditional maximum entropy framework with the conventional maximum likelihood framework, and also clarify how the first order phase transitions in LBP's for Potts models influence our hyperparameter estimation procedures.

We introduce the Concave-Convex procedure (CCCP) which constructs discretetime iterative dynamical systems which are guaranteed to monotonically decrease global optimization/energy functions. It can be applied to (almost) any optimization problem and many existing algorithms can be interpreted in terms of CCCP. In particular, we prove relationships to some applications of Legendre transform techniques. We then illustrate CCCP by applications to Potts models, linear assignment, EM algorithms, and Generalized Iterative Scaling (GIS). CCCP can be used both as a new way to understand existing optimization algorithms and as a procedure for generating new algorithms. 1 Introduction There is a lot of interest in designing discrete time dynamical systems for inference and learning (see, for example, [10], [3], [7], [13]).

A new approach for clustering is proposed. This method is based on an analogy to a physical model; the ferromagnetic Potts model at thermal equilibrium is used as an analog computer for this hard optimization problem. We do not assume any structure of the underlying distributionof the data. Phase space of the Potts model is divided into three regions; ferromagnetic, super-paramagnetic and paramagnetic phases. The region of interest is that corresponding to the super-paramagnetic one, where domains of aligned spins appear.

Several of the workshops have led to discourse? At what levels of the art in the field (Dale et al. 1992; generation is information processed on Natural Language Paris, Swartout, and Mann 1991; How can we generate to 24 June 1994 at the Nonantum The goal of this latest workshop multilingual texts efficiently? Inn on the seacoast in Kennebunkport, was to introduce new, cutting-edge The topics presented at the workshop Maine. Two invited speakers described subtopics such as evaluation, casual site contributed greatly to their perspectives on two areas outside explanation generation, and summarization the success of the workshop in stimulating the field that might become an occur with increasing frequency the exchange of ideas. Pustejovsky (Brandeis University) presented Different generator designers make that any individual generation his views on the richness of different choices, and the resulting project should define--in its own what can be encoded in what he calls systems are hard to compare.