Weighted and Robust Incremental Method for Subspace Learning

Authors:
Danijel Skocaj;Ales Leonardis
Affiliations:
-;-
Venue:
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Year:
2003

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Abstract

Visual learning is expected to be a continuous and robustprocess, which treats input images and pixels selectively.In this paper we present a method for subspace learning,which takes these considerations into account. Wepresent an incremental method, which sequentially updatesthe principal subspace considering weighted influence ofindividual images as well as individual pixels within an image.This approach is further extended to enable determinationof consistencies in the input data and imputation of thevalues in inconsistent pixels using the previously acquiredknowledge, resulting in a novel incremental, weighted androbust method for subspace learning.