Multiple eigenspaces
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 imagesseeds. 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.
multiple eigenspaces appearance-based object representation principal component analysis (pca) visual learning image grouping appearance-based object recognition dimensionality reduction view-based navigation map