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Representing outliers for improved multi-spectral data reduction

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Titel Representing outliers for improved multi-spectral data reduction
Alternativer Titel Representing outliers for improved multi spectral data reduction
Serientitel The Sixth European Conference on Colour in Graphics, Imaging, and Vision (CGIV 2012)
Teil 22
Anzahl der Teile 31
Autor Agahian, Farnaz
Funt, Brian
Amirshahi, Seyed Hossein
Lizenz CC-Namensnennung - keine Bearbeitung 2.0 UK: England & Wales:
Sie dürfen das Werk in unveränderter Form zu jedem legalen Zweck nutzen, vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
DOI 10.5446/31463
Herausgeber River Valley TV
Erscheinungsjahr 2012
Sprache Englisch

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Fachgebiet Informatik
Abstract Large multi-spectral datasets such as those created by multi-spectral images require a lot of data storage. Compression of these data is therefore an important problem. A common approach is to use principal components analysis (PCA) as a way of reducing the data requirements as part of a lossy compression strategy. In this paper, we employ the fast MCD (Minimum Covariance Determinant) algorithm, as a highly robust estimator of multivariate mean and covariance, to detect outlier spectra in a multi-spectral image. We then show that by removing the outliers from the main dataset, the performance of PCA in spectral compression significantly increases. However, since outlier spectra are a part of the image, they cannot simply be ignored. Our strategy is to cluster the outliers into a small number of groups and then compress each group separately using its own cluster-specific PCA-derived bases. Overall, we show that significantly better compression can be achieved with this approach.

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