Clustering for large-scale segmentation dataset collection
Name
1220836817-MIT.pdf
Size
9 MB
Format
Adobe PDF
Checksum (MD5)
b4113c42d0bab306f2667da0d874ee39
Author(s)
Hu, Jeffrey(Jeffrey H.)
Advisor(s)
Antonio Torralba.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Segmentation datasets are smaller and much more expensive to collect than their image classification counterparts. Leveraging machine learning in the annotation process will be critical to scaling these datasets up. In this thesis, we propose an iterative cluster-based approach to segmentation data collection. By using existing networks to predict millions of segmentations and clustering to group similar predictions together, we ask human annotators a small number of questions per cluster and collect a large number of reasonable-quality segmentations at low cost. Although the collected segmentations are biased towards objects already predicted by the network, we demonstrate that they improve performance upon re-training and that the procedure can be applied iteratively, up to a point, to discover harder and harder objects. We demonstrate this pipeline in simulation and show promising results on real unlabeled images. We also present a new annotation tool called LabelMeLite for the rapid filtering and editing of predicted segmentations.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2019
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 35-36).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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