Algorithms for selecting parameters of combination of acyclic adjacency graphs in the problem of texture image processing

Dinh Sang


Nowadays the great interest of researchers in the problem of processing the interrelated data arrays including images is retained. In the modern theory of machine learning, the problem of image processing is often viewed as a problem in the field of graph models. Image pixels constitute a unique array of interrelated elements. The interrelations between array elements are represented by an adjacency graph. The problem of image processing is often solved by minimizing Gibbs energy associated with corresponding adjacency graphs. The crucial disadvantage of Gibbs approach is that it requires empirical specifying of appropriate energy functions on cliques. In the present work, we investigate a simpler, but not less effective model, which is an expansion of the Markov chain theory. Our approach to image processing is based on the idea of replacing the arbitrary adjacency graphs by tree-like (acyclic in general) ones and linearly combining of acyclic Markov models in order to get the best quality of restoration of hidden classes. In this work, we propose algorithms for tuning combination of acyclic adjacency graphs.


Image Processing; Image Segmentation; Supervised Learning; Hidden Markov Model; Markov Chain; Graph Model
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