Aggregating crowdsourced image segmentations

Doris Jung Lin Lee, Akash Das Sarma, Aditya Parameswaran

Research output: Contribution to journalConference article

Abstract

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 languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume2173
StatePublished - Jan 1 2018
Event2018 HCOMP Works in Progress and Demonstration Papers, HCOMP WIP and DEMO 2018 - Zurich, Switzerland
Duration: Jul 5 2018Jul 8 2018

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Lee, D. J. L., Sarma, A. D., & Parameswaran, A. (2018). Aggregating crowdsourced image segmentations. CEUR Workshop Proceedings, 2173.