Segmentation of Random Fields Via Borrowed Strength Density Estimation

In many applications, spatial observations must be segmented into homogeneous regions and the number, positions, and shapes of the regions are unknown a priori. Information about the underlying probability distributions are often unknown. Furthermore, the anticipated regions of interest may be small with few observations from the individual regions. This paper presents a technique designed to address these difficulties. A simple segmentation procedure can be obtained as a clustering of the disjoint subregions obtained through an initial low-level partitioning procedure. Clustering of these subregions based upon a similarity matrix derived from estimates of their marginal probability density functions yields the resultant segmentation. It is shown that this segmentation is improved through the use of a "borrowed strength" density estimation procedure wherein potential similarities between the density functions for the subregions are exploited. The borrowed strength technique is described and the performance of segmentation based on these estimates is investigated through an example from statistical image analysis.