(An) integrated approach for image understanding based on markov random field model

Abstract

In this thesis, we propose a Markov Random Field based approach as a unified and systematic way for modeling, encoding and applying scene knowledge to the image understanding problem. Generally the goal of the image understanding is achieved by two major processes; image segmentation and interpretation. So far most image understanding systems have adopted the knowledge-based approach inference component of which is typically rule-based, and they still follow a hypothesize-and-test paradigm. In those systems the interpretation and segmentation processes are separated and their incorporation is achieved through intermediate form of results such as hypotheses and evidences. And there is not usually mentioned about the explicit procedure for adjusting mis-labels when the evidences or judgements are wrong. Instead, the region analysis had to be propagated from the regions with established judgement to the nearby regions in similar fashion with region growing technique. Thus in our approach we formulate the image segmentation and interpretation problem as a unified way and solve it through a general optimization algorithm. That is, in the proposed scheme, the image is first segmented into a set of disjoint regions by traditional region-based segmentation technique which operates on image pixels. Our scheme then proceeds on the initial set of segmented regions by defining the image segmentation and interpretation problem based on the MRF models. In the MRF model we specify the a priori knowledge about the optimal segmentation and interpretation in the form of clique functions and those clique functions are incorporated into a unified energy function to be minimized by optimization. More specifically, for the case of image segmentation, the clique functions encode the constraints that a single segmented region should be uniform in spectral features and there should exist salient discontinuities on the common boundary between adjacent regions. And also for the case of i...