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Quality Control in Crowdsourced Object Segmentation

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.
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Submitted on : Thursday, March 3, 2016 - 11:21:04 AM
Last modification on : Monday, July 4, 2022 - 8:51:44 AM
Long-term archiving on: : Saturday, June 4, 2016 - 10:42:55 AM


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  • HAL Id : hal-01282046, version 1
  • OATAO : 15073


Ferran Cabezas, Axel Carlier, Vincent Charvillat, Amaia Salvador, Xavier Giro I Nieto. Quality Control in Crowdsourced Object Segmentation. IEEE International Conference on Image Processing (ICIP 2015), Sep 2015, Québec, Canada. pp.4243-4247. ⟨hal-01282046⟩



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