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Inferring Ground Truth from Subjective Labelling of Venus Images

Smyth, Padhraic and Fayyad, Usama and Burl, Michael and Perona, Pietro and Baldi, Pierre (1995) Inferring Ground Truth from Subjective Labelling of Venus Images. In: Advances in Neural Information Processing Systems 7. The MIT Press , Cambridge, MA, pp. 1085-1092. ISBN 0-262-20104-6. https://resolver.caltech.edu/CaltechAUTHORS:20150305-153627706

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Abstract

In remote sensing applications "ground-truth" data is often used as the basis for training pattern recognition algorithms to generate thematic maps or to detect objects of interest. In practical situations, experts may visually examine the images and provide a subjective noisy estimate of the truth. Calibrating the reliability and bias of expert labellers is a non-trivial problem. In this paper we discuss some of our recent work on this topic in the context of detecting small volcanoes in Magellan SAR images of Venus. Empirical results (using the Expectation-Maximization procedure) suggest that accounting for subjective noise can be quite significant in terms of quantifying both human and algorithm detection performance.


Item Type:Book Section
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URLURL TypeDescription
http://papers.nips.cc/paper/949-inferring-ground-truth-from-subjective-labelling-of-venus-imagesOrganizationArticle
ORCID:
AuthorORCID
Perona, Pietro0000-0002-7583-5809
Additional Information:© 1995 Massachusetts Institute of Technology. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration and was supported in part by ARPA under grant number N00014-92-J-1860.
Funders:
Funding AgencyGrant Number
NASAUNSPECIFIED
Office of Naval Research (ONR)N00014-92-J-1860
Advanced Research Projects Agency (ARPA)UNSPECIFIED
Record Number:CaltechAUTHORS:20150305-153627706
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20150305-153627706
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:55562
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:06 Mar 2015 05:29
Last Modified:03 Oct 2019 08:06

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