The presence of clutter complicates the location of targets in time series and images. Various types of adaptive clutter model have been proposed to deal with this problem. In this paper we treat clutter as a type of texture, and we propose a novel type of hierarchical Gibbs distribution texture model. To optimize this type of model, we define a relative entropy cost function that we decompose into a sum over a number of terms, each of which can be interpreted as the mutual information between clusters of samples of the data. Furthermore, we show how the various terms of this cost function can be used to construct an image-like representation of the relative entropy. Finally, using a Brodatz texture image, we present an example of this type of decomposition and demonstrate that a statistical anomaly in the Brodatz texture image can be easily located.