Semantic categorization of outdoor scenes with uncertainty estimates using multi-class gaussian process classification
Name
paul12semantic.pdf
Description
Accepted version
Size
1.52 MB
Format
Adobe PDF
Checksum (MD5)
1b471830f95535e561cb3cd2681e41d9
Author(s)
Paul, Rohan
Triebel, Rudolph
Rus, Daniela L
Newman, Paul
Date Issued
December 2012
Journal
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Paul, Rohan et al. "Semantic categorization of outdoor scenes with uncertainty estimates using multi-class gaussian process classification." 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2012, Vilamoura, Portugal, Institute of Electrical and Electronics Engineers, December 2012. © 2012 IEEE
Version
Author's final manuscript
Abstract
This paper presents a novel semantic categorization method for 3D point cloud data using supervised, multiclass Gaussian Process (GP) classification. In contrast to other approaches, and particularly Support Vector Machines, which probably are the most used method for this task to date, GPs have the major advantage of providing informative uncertainty estimates about the resulting class labels. As we show in experiments, these uncertainty estimates can either be used to improve the classification by neglecting uncertain class labels or - more importantly - they can serve as an indication of the under-representation of certain classes in the training data. This means that GP classifiers are much better suited in a lifelong learning framework, where not all classes are represented initially, but instead new training data arrives during the operation of the robot. © 2012 IEEE.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iros.2012.6386073
