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.