Learning Inhomogeneous FRAME Models for Object Patterns
This research investigates an inhomogeneous version of the FRAME (Filters, Random field, And Maximum Entropy) model and apply it to modeling object patterns. The inhomogeneous FRAME is a non-stationary Markov random field model that reproduces the observed marginal distributions or statistics of filter responses at all the different locations, scales and orientations. The experiments show that the inhomogeneous FRAME model is capable of generating a wide variety of object patterns in natural images. It is useful for object detection, alignment, and clustering.