Tree-based binary image dissimilarity measure with meta-heuristic optimization

Abstract

In this paper, we present a method of evaluating binary image dissimilarity based on tree representation and heuristic optimization. Starting from the image, a graph structure of a binary tree is constructed that splits the set of image foreground pixels into consecutive subsets attached to tree nodes. Next, instead of comparing two images themselves, one compares the trees and expresses image dissimilarity as tree dissimilarity, which can be characterized by a nonlinear function. The goal is to find its minimum, as it corresponds with the best match of compared trees. Searching for the minimum would be ineffective with analytical optimization methods. Hence, we have approached the issue with three meta-heuristic algorithms, namely genetic algorithm, particle swarm optimization (PSO) and simulated annealing. The presented results show that PSO achieved the best results. The proposed method is compared with other binary image comparison approaches. The performed tests that are described in the paper show that it outperforms its competitors and can be successfully applied to compare binary images.

Qualitative and Quantitative Analysis of Definitions of e-Health and m-Health

“…Therefore, to increase the reliability of the results, this study and future studies could use the "interrater agreement" in R. The function is used when all persons who perform a study make individual interpretations, which are then cross tabulated to learn to what extent their proposed thematizations match each other. Both the validity and reliability could have been increased further if a reference algorithm had been used to determine whether the analysis of the same definitions had generated the same results with different measuring instruments and more analyses [43].…”