Year: 2015
Type: conference-paper
Abstract: 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.
Authors: Ferran Cabezas, Axel Carlier, Vincent Charvillat, Amaia Salvador, Xavier Giro-i-Nieto
Institutions: Université de Toulouse, Universitat Politècnica de Catalunya
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1574679496