  This paper explores processing techniques to deal with noisy data in
crowdsourced object segmentation tasks. We use the data collected with
"Click'n'Cut", an online interactive segmentation tool, and we perform several
experiments towards improving the segmentation results. First, we introduce
different superpixel-based techniques to filter users' traces, and assess their
impact on the segmentation result. Second, we present different criteria to
detect and discard the traces from potential bad users, resulting in a
remarkable increase in performance. Finally, we show a novel superpixel-based
segmentation algorithm which does not require any prior filtering and is based
on weighting each user's contribution according to his/her level of expertise.
