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Representing outliers for improved multi-spectral data reduction
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Metadaten
Formale Metadaten
| 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 |
Inhaltliche Metadaten
| 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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