Instance-level image segmentation provides rich information crucial for scene understanding in a variety of real-world applications. In this paper, we evaluate multiple crowdsourced algorithms for the image segmentation problem, including novel worker-aggregation-based methods and retrieval-based methods from prior work. We characterize the different types of worker errors observed in crowdsourced segmentation, and present a clustering algorithm as a preprocessing step that is able to capture and eliminate errors arising due to workers having different semantic perspectives. We demonstrate that aggregation-based algorithms attain higher accuracies than existing retrieval-based approaches, while scaling better with increasing numbers of worker segmentations.
|Original language||English (US)|
|Journal||CEUR Workshop Proceedings|
|State||Published - Jan 1 2018|
|Event||2018 HCOMP Works in Progress and Demonstration Papers, HCOMP WIP and DEMO 2018 - Zurich, Switzerland|
Duration: Jul 5 2018 → Jul 8 2018
ASJC Scopus subject areas
- Computer Science(all)