A semi-supervised learning algorithm for recognizing sub-classes

Ranga Raju Vatsavai, Shashi Shekhar, Budhendra Bhaduri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In many practical situations it is not feasible to collect labeled samples for all available classes in a domain. Especially in supervised classification of remotely sensed images it is impossible to collect ground truth information over large geographic regions for all thematic classes. As a result often analysts collect labels for aggregate classes (e.g., Forest, Agriculture, Urban). In this paper we present a novel learning scheme that automatically learns sub-classes (e.g., Hardwood, Conifer) from the user given aggregate classes. We model each aggregate class as finite Gaussian mixture instead of classical assumption of unimodal Gaussian per class. The number of components in each finite Gaussian mixture are automatically estimated. A semi-supervised learning is then used to recognize subclasses by utilizing very few labeled samples per each sub-class and a large number of unlabeled samples. Experimental results on real remotely sensed image classification showed not only improved accuracy in aggregate class classification but the proposed method also recognized sub-classes accurately.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
Pages458-467
Number of pages10
DOIs
StatePublished - Dec 1 2008
EventIEEE International Conference on Data Mining Workshops, ICDM Workshops 2008 - Pisa, Italy
Duration: Dec 15 2008Dec 19 2008

Publication series

NameProceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008

Other

OtherIEEE International Conference on Data Mining Workshops, ICDM Workshops 2008
CountryItaly
CityPisa
Period12/15/0812/19/08

Keywords

  • EM
  • GMM
  • Remote sensing
  • Semi-supervised learning

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