@conference {cCabezas, title = {Quality Control in Crowdsourced Object Segmentation}, booktitle = {IEEE International Conference on Image Processing (ICIP), 2015}, year = {2015}, month = {09/2015}, abstract = {

This paper explores processing techniques to deal with noisy data in crowdsourced object segmentation tasks. We use the data collected with "Click{\textquoteright}n{\textquoteright}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{\textquoteright} 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{\textquoteright}s contribution according to his/her level of expertise.

Selected among Top 10\% papers in ICIP 2015 based on the reviewer scores and recommendations.

[Preprint on arXiv]

[Related BSc thesis by Ferran Cabezas]

[IEEE ICIP 2015 conference website]

Co-filtering human interaction and object segmentation from Xavier Giro

}, url = {http://arxiv.org/abs/1505.00145}, author = {Cabezas, Ferran and Carlier, Axel and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Charvillat, Vincent} }