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Unsupervised Alignment of Image Manifolds with Centrality Measures

Authors :
Devis Tuia
Gustau Camps-Valls
Michele Volpi
Source :
ICPR, Proceedings-International Conference on Pattern Recognition. Institute of Electrical and Electronics Engineers Inc., Proceedings-International Conference on Pattern Recognition
Publication Year :
2014
Publisher :
IEEE, 2014.

Abstract

The re use of available labeled samples to classify newly acquired data is a hot topic in pattern analysis and machine learning. Classification or regression algorithms developed with data from one domain cannot be directly used in another related domain unless the data representation or the classifier have been adapted to the new data distribution. This is crucial in satellite/airborne image analysis: when confronted to domain shifts issued from changes in acquisition or illumination conditions image classifiers tend to become inaccurate. In this paper we introduce a method to align data manifolds that represent the same land cover classes but have undergone spectral distortions. The proposed method relies on a recently proposed semi supervised manifold alignment technique. We propose an algorithm to relax the strong requirement of labeled data in all domains by exploiting centrality measures over graphs to match the manifolds. Experiments on multispectral pixel classification at very high spatial resolution show the potential of the method.

Details

ISBN :
978-1-4799-5208-3
ISBNs :
9781479952083
Database :
OpenAIRE
Journal :
2014 22nd International Conference on Pattern Recognition
Accession number :
edsair.doi.dedup.....7371d89af7dfc2657ebf94ca0b84cc0b
Full Text :
https://doi.org/10.1109/icpr.2014.167