Transfer component analysis for domain adaptation in image classification

Giona Matasci*, Michele Volpi, Devis Tuia, Mikhail Kanevski

*Corresponding author for this work

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

7 Citations (Scopus)

Abstract

This contribution studies a feature extraction technique aiming at reducing differences between domains in image classification. The purpose is to find a common feature space between labeled samples issued from a source image and test samples belonging to a related target image. The presented approach, Transfer Component Analysis, finds a transformation matrix performing a joint mapping of the two domains by minimizing a probability distribution distance measure, the Maximum Mean Discrepancy criterion. When predicting on a target image, such a projection allows to apply a supervised classifier trained exclusively on labeled source pixels mapped in this common latent subspace. Promising results are observed on a urban scene captured by a hyperspectral image. The experiments reveal improvements with respect to a standard classification model built on the original source image and other feature extraction techniques.

Original languageEnglish
Title of host publicationImage and Signal Processing for Remote Sensing XVII
DOIs
Publication statusPublished - 26 Oct 2011
Externally publishedYes
EventImage and Signal Processing for Remote Sensing XVII - Prague, Czech Republic
Duration: 19 Sep 201121 Sep 2011

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8180
ISSN (Print)0277-786X

Conference

ConferenceImage and Signal Processing for Remote Sensing XVII
CountryCzech Republic
CityPrague
Period19/09/1121/09/11

Keywords

  • Domain adaptation
  • Feature extraction
  • Image classification
  • Transfer Component Analysis

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  • Cite this

    Matasci, G., Volpi, M., Tuia, D., & Kanevski, M. (2011). Transfer component analysis for domain adaptation in image classification. In Image and Signal Processing for Remote Sensing XVII [81800F] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8180). https://doi.org/10.1117/12.898229