Evaluating Outlines as a Means to Segment Stains — A User Study

Training supervised deep learning approaches requires a huge amount of labeled data. In the case of object segmentation on images, the creation of labeled data is expensive. Thus, creating a simple, fast and intuitive labeling process is of interest. Such an approach for object segmentation is to outline objects on a touch interface. However, outlines drawn on a touch device are too rough to be used as labels directly. To improve this, interactive segmentation algorithms can be applied. The results can be improved by utilizing prior information about the outline. In this paper, we examine what kind of information about user-drawn outlines can be used to improve the results of interactive segmentation algorithms on the use-case of segmenting stains on images of laundry. To do this, a user study has been conducted in which participants had to draw outlines on ten different images with varying amounts of stains. Our findings suggest, that 1) outlines often contain multiple stains, 2) outlines often cut through stains and 3) the shape of an outline is seldom similar to the shape of the contained stains. Thus, many assumptions integrated into established interactive segmentation algorithms do not hold which prevents their usage in a fast and intuitive labeling tool.