A learning scheme for recognizing sub-classes from model trained on aggregate classes

Ranga Raju Vatsavai, Shashi Shekhar, Budhendra Bhaduri

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

3 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. In this paper we present a novel learning scheme that automatically learns sub-classes 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. 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.

Original languageEnglish (US)
Title of host publicationStructural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, SSPR and SPR 2008, Proceedings
Pages967-976
Number of pages10
DOIs
StatePublished - 2008
EventJoint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, SSPR and SPR 2008 - Orlando, FL, United States
Duration: Dec 4 2008Dec 6 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5342 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherJoint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, SSPR and SPR 2008
CountryUnited States
CityOrlando, FL
Period12/4/0812/6/08

Keywords

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

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