A new challenging image dataset with simple background for evaluating and developing co-segmentation algorithms

Many image co-segmentation algorithms have been proposed over the last decade. In this paper, we present a new dataset for evaluating co-segmentation algorithms, which contains 889 image groups with 18 images in each and the pixel-wise hand-annotated ground truths. The dataset is characterized by simple background produced from nearly a single color. It looks simple but is actually very challenging for current co-segmentation algorithms, because of four difficult cases in it: easy-confused foreground with background, transparent regions in objects, minor holes in objects, and shadows. In order to test the usefulness of our dataset, we review the state-of-the-art co-segmentation algorithms and evaluate seven algorithms on our dataset. The obtained performance of each algorithm is compared with those previously reported in the datasets with complex background. The results prove that our dataset is valuable for the development of co-segmentation techniques. It is more feasible to solve the four difficulties above on the simple background and then extend the solutions to the complex background problems. Our dataset can be freely downloaded from: http://www.iscbit.org/source/MLMR-COS.zip.