Superpixel segmentation: A benchmark

Various superpixel approaches have been published recently. These algorithms are assessed using different evaluation metrics and datasets resulting in discrepancy in algorithm comparison. This calls for a benchmark to compare the state-of-the-arts methods and evaluate their pros and cons. We analyze benchmark metrics, datasets and built a superpixel benchmark. We evaluated and integrated top 15 superpixel algorithms, whose code are publicly available, into one code library and, provide a quantitative comparison of these algorithms. We find that some superpixel algorithms perform consistently better than others. Clustering based superpixel algorithms are more efficient than graph-based ones. Furthermore, we also introduced a novel metric to evaluate superpixel regularity, which is a property that superpixels desired. The evaluation results demonstrate the performance and limitations of state-of-the-art algorithms. Our evaluation and observations give deep insight about different algorithms and will help researchers to identify the more feasible superpixel segmentation methods for their different problems.