Quality Control in Crowdsourced Object Segmentation

Ferran Cabezas (1) , Axel Carlier (2, 3) , Vincent Charvillat (2, 3) , Amaia Salvador (4) , Xavier Giro I Nieto (4)

1 IRIT-VORTEX - Visual Objects from Reality To Expression
2 IRIT-REVA - Real Expression Artificial Life
3 Toulouse INP - Institut National Polytechnique (Toulouse)
4 UPC - Universitat Politècnica de Catalunya = Université polytechnique de Catalogne [Barcelona]

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.

Mots clés: Interactive segmentation; Superpixel; Object Segmentation; Crowdsourcing; Quality control

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

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⟩