A computationally efficient evaluation environment for image segmentation

An emphasis on quantitative testing of segmentation algorithms implies rigorous testing against ground truth segmentations. When testing extends over the algorithm´s parameter space, then the search for a best fit has a considerable cost in time. The paper reports wide variety both in evaluation time and segmentation results for an example mean-shift algorithm. This paper proposes a three-component computation environment aimed at automating the search and reducing the evaluation time. The first component relies on scripted testing and collation of results. The second component transfers to a commodity cluster computer. And the third component introduces a genetic algorithm to avoid an exhaustive search. Application of the genetic algorithm drastically reduces search times.