Block-Wise Markov Models

Abstract

The multiscale Markov models introduced in Chapter 4 can be used to extract large scale image features by parameterizing inter scale and intra scale interactions in an image. Since there are multiple scales with different parameter values for different scales, the parameter estimation process in the multi-scale approach becomes a major computational burden. One way to alleviate this complexity is to adopt a block-wise image modeling paradigm. That is, dividing the image space into non-overlapping blocks, a random variable is assigned for each image block to represent the class label. Then, a representative feature for each image block is extracted and is treated as the observed image data. The collection of all block features constitute the realization of the random field Y. Also, the class label assigned for each image block is a realization of the unobservable random field X. Since the representative feature values for image blocks normally have spatial continuity, the random field for the block class labels can be modeled as an MRF.