Recently, the quantitative evaluation of interactive single image matting techniques has become possible by the
introduction of high-quality ground truth datasets. However, quantitative comparisons conducted in previous
work are based on error metrics (e.g. sum of absolute differences) that are not necessarily correlated to the visual
quality of the image as perceived by the user. This motivates research to better understand the perception of
errors inherent to matting algorithms, in order to provide the ground for a future design of error metrics that
better reflect the subjective impression of the human observer.
In this work we gain novel insights into the perception of errors due to imperfect matting results. To investigate
these errors, we compare two recent state-of-the-art matting algorithms in a user study. We use an eye-tracker
to reveal details of the decision making of the users. The data acquired in the user study show a considerable
correlation between expert knowledge in photography and the ability of the user to detect errors in the image.
This is also reflected in the eye-tracking data which reveals different types of scanning paths dependent on the
experience of the user.