NDLI: Towards autonomous habitat classification using Gaussian Mixture Models

Content Provider	IEEE Xplore Digital Library
Author	Steinberg, D.M. Williams, S.B. Pizarro, O. Jakuba, M.V.
Copyright Year	2010
Description	Author affiliation: Australian Centre for Field Robotics (ACFR), University of Sydney, NSW 2006, Australia (Steinberg, D.M.; Williams, S.B.; Pizarro, O.; Jakuba, M.V.)
Abstract	Robotic agents that can explore and sample in a completely unsupervised fashion could greatly increase the amount of scientific data gathered in dangerous and inaccessible environments. Our application is imaging the benthos using an autonomous underwater vehicle with limited communication to surface craft. Robotic exploration of this nature demands in situ data analysis. To this end, this paper presents results of using a Gaussian Mixture Model (GMM), a Hidden Markov Model (HMM) filter, an Infinite Gaussian Mixture Model (IGMM) and a Variation Dirichlet Process model (VDP) for the classification of benthic habitats. All of the models are trained using unsupervised methods. Furthermore, the IGMM and VDP are trained without knowing the the number of classes in the dataset. It was found that the sequential information the HMM filter provides to the classification process adds lag to the habitat boundary estimates, reducing the classification accuracy. The VDP proved to be the most accurate classifier of the four tested, and also one of the fastest to train. We conclude that the VDP is a powerful model for entirely autonomous labelling of benthic datasets.
Starting Page	4424
Ending Page	4431
File Size	2112649
Page Count	8
File Format	PDF
ISBN	9781424466740
ISSN	21530866
e-ISBN	9781424466764
DOI	10.1109/IROS.2010.5652480
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-18
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Training Classification algorithms Data models Graphical models Approximation algorithms Approximation methods
Content Type	Text
Resource Type	Article

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