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Quality Evaluation Methods for Crowdsourced Image Segmentation

Jung-Lin Lee, Doris and Das Sarma, Akash and Parameswaran, Aditya Quality Evaluation Methods for Crowdsourced Image Segmentation. Technical Report. Stanford InfoLab.


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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 charac- terize the different types of worker errors observed in crowdsourced segmentation, and present a clustering algorithm as a preprocess- ing 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 exist- ing retrieval-based approaches, while scaling better with increasing numbers of worker segmentations.

Item Type:Techreport (Technical Report)
ID Code:1161
Deposited By:Akash Das Sarma
Deposited On:03 Jun 2018 17:01
Last Modified:03 Jun 2018 17:04

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