Sub-class recognition from aggregate class labels: Preliminary results

Ranga Raju Vatsavai, Budhendra Bhaduri, Shashi Shekhar

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

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 subclasses 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 subclasses.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
Pages61-64
Number of pages4
DOIs
StatePublished - Dec 22 2008
Event20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 - Dayton, OH, United States
Duration: Nov 3 2008Nov 5 2008

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume1
ISSN (Print)1082-3409

Other

Other20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08
CountryUnited States
CityDayton, OH
Period11/3/0811/5/08

Keywords

  • EM
  • GMM
  • Remote sensing

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  • Cite this

    Vatsavai, R. R., Bhaduri, B., & Shekhar, S. (2008). Sub-class recognition from aggregate class labels: Preliminary results. In Proceedings - 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI'08 (pp. 61-64). [4669672] (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI; Vol. 1). https://doi.org/10.1109/ICTAI.2008.152