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26 October 2011 Transfer component analysis for domain adaptation in image classification
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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.
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Giona Matasci, Michele Volpi, Devis Tuia, and Mikhail Kanevski "Transfer component analysis for domain adaptation in image classification", Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800F (26 October 2011); https://doi.org/10.1117/12.898229
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