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Technical Program
Paper Detail
| Paper: | MP-P5.4 |
| Session: | Machine Learning for Image and Video Classification |
| Time: | Monday, October 9, 14:20 - 17:00 |
| Presentation: |
Poster
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| Title: |
AUTOMATIC MODEL-ORDER SELECTION FOR PCA |
| Authors: |
Michel Sarkis; LDV - TUM | | | | | Zaher Dawy; American University of Beirut | | | | | Florian Obermeier; LDV - TUM | | | | | Klaus Diepold; LDV - TUM | | |
| Abstract: |
Determining the model-order of a given data set is an important task in signal analysis. Principal Component Analysis (PCA) can be used for this purpose if there is a criterion upon which the correct order can be chosen. In this work, we propose a new and simple technique to determine automatically the rank of a PCA model. Tested with simulated data, the algorithm is able to determine the correct model order efficiently. Applied to video sequences, this method is able to estimate the necessary subspaces that capture the motion and illuminance changes within the different frames. This helps in reducing the storage need/requirements of video sequences and improves the efficiency of context based search and retrieval techniques. |
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