Show simple item record

dc.contributor.authorGirdhar, Yogesh
dc.contributor.authorFlaspohler, Genevieve Elaine
dc.contributor.authorRoy, Nicholas
dc.date.accessioned2018-05-30T15:57:51Z
dc.date.available2018-05-30T15:57:51Z
dc.date.issued2017-12
dc.identifier.isbn978-1-5386-2682-5
dc.identifier.urihttp://hdl.handle.net/1721.1/115970
dc.description.abstract© 2017 IEEE. The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate. Recent algorithmic attempts to fill this gap have employed unsupervised tools to discover structure in data. Some of the most successful approaches have used probabilistic models to uncover latent thematic structure in discrete data. Despite the success of these models on textual data, they have not generalized as well to image data, in part because of the spatial and temporal structure that may exist in an image stream. We introduce a novel unsupervised machine learning framework that incorporates the ability of convolutional autoencoders to discover features from images that directly encode spatial information, within a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data. By using this hybrid framework, we overcome the fundamental dependency of traditional topic models on rigidly hand-coded data representations, while simultaneously encoding spatial dependency in our topics without adding model complexity. We apply this model to the motivating application of high-level scene understanding and mission summarization for exploratory marine robots. Our experiments on a seafloor dataset collected by a marine robot show that the proposed hybrid framework outperforms current state-of-the-art approaches on the task of unsupervised seafloor terrain characterization.en_US
dc.description.sponsorshipNSF Graduate Research Fellowship Program awarden_US
dc.description.sponsorshipThe John P. Chase Memorial Endowed Funden_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/IROS.2017.8202130en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleFeature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic modelsen_US
dc.typeArticleen_US
dc.identifier.citationFlaspohler, Genevieve, Nicholas Roy, and Yogesh Girdhar. “Feature Discovery and Visualization of Robot Mission Data Using Convolutional Autoencoders and Bayesian Nonparametric Topic Models.” 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (September 2017).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorFlaspohler, Genevieve Elaine
dc.contributor.mitauthorRoy, Nicholas
dc.relation.journal2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2018-04-09T17:34:56Z
dspace.orderedauthorsFlaspohler, Genevieve; Roy, Nicholas; Girdhar, Yogeshen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-8293-0492
mit.licenseOPEN_ACCESS_POLICYen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record