Time-Weighted Evaluation of Image Segmentation With a Genetic Algorithm

Abstract

Abstract:Quantitative evaluation of segmentation algorithms implies rigorous testing against ground truth segmentations. Though under-reported in the literature, the performance of a segmentation algorithm depends on the choice of input parameters. When testing extends over an algorithms parameter space, then the search for satisfactory settings has a considerable cost in time. This paper considers the use of a genetic algorithm (GA) to avoid an exhaustive search. As application of the GA drastically reduces search times, the paper investigates how best to apply the GA in terms of initial candidate population, convergence speed, and application of a final polishing round. To further reduce evaluation time and subsequent image batch-processing times, this paper introduces a time factor into the GA cost function. It is found that this procedure while preserving the GA solution also improves interpretation and parameter selection.