Combinatorial enlargement of ground-truth datasets and efficient evaluation of segmentation algorithms

We propose a method to exponentially enlarge a small dataset of domain specific ground truth segmentation labels to evaluate the performance of segmentation algorithms. Furthermore, we adapt ideas from combinatorial software testing to efficiently infer statistics of segmentation performance by evaluating performance on only a certain subset of the combinatorially generated images. Extensions of this work to optimal sequence for performance testing and algorithm selection are also suggested.