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Multiple eigenspaces
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DOI：10.1016/S0031-3203(01)00198-4.png)
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摘要
En 中文
In this paper, we propose a novel self-organizing framework to construct multiple, low-dimensional eigenspaces from a set of training images. Grouping of images is systematically and robustly performed via eigenspace-growing in terms of low-dimensional eigenspaces. To further increase the robustness, the eigenspace-growing is initiated independently with many small groups of images-seeds. All these grown eigenspaces are treated as hypotheses that are subject to a selection procedure eigenspace-selection, based on the MDL principle, which selects the final resulting set of eigenspaces as an efficient representation of the training set, taking into account the number of images encompassed by the eigenspaces, the dimensions of the eigenspaces, and their corresponding residual errors. We have tested the proposed method on a number of standard image sets, and the significance of the approach with respect to the recognition rate has been demonstrated. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keyword:
multiple eigenspaces
appearance-based object representation
principal component analysis (PCA)
visual learning
image grouping
appearance-based object recognition
dimensionality reduction
view-based navigation map
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7.6
论文数:
1.3W
被引数:
4.5W
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