A COMPARATIVE STUDY OF DECISION MAKING ALGORITHMS IN IMAGES MODELED BY GAUSSIAN MARKOV RANDOM FIELDS

Abstract
In this work the performance and computer time requirements of 15 classifiers are compared in images modeled by two-dimensional Gaussian Markov random fields which are represented by a causal autoregressive model of the second order. The per-pixel classifier and the object classifier directly or indirectly utilizing spectral-spatial characteristies of images are among them. The probability of misclassification (PMC) calculated analytically and experimentally on modeled data was used as a measure of a classifier performance. The influence of such factors as the object size and form, the inadequacy of a classifier and data models, the accuracy of spatial correlation estimation on the PMC is investigated. The following main results are obtained. The performance of object classifiers is much better than that of per-pixel classifiers. The PMC of object classifiers decreases rapidly with the increase of the size of an object. The performance of object classifiers indirectly incorporating spatial characteristics of an object (OCIND) and that of object classifiers directly incorporating spatial characteristics (OCDIR) is similar for the linear decision rule. The performance of OCDIR is much better than that of OCIND for the quadratic decision rule. Computationally, averaging object classifiers are fastest, next are OCIND and finally OCDIR. The cross-shaped object classifier is better and faster than the square-shaped object classifier for the same number of pixels.

This publication has 0 references indexed in Scilit: