Incremental and robust learning of subspace representations

Authors:
Danijel Skočaj;Aleš Leonardis
Affiliations:
Visual Cognitive Systems Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Traška 25, SI-1001 Ljubljana, Slovenia;Visual Cognitive Systems Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Traška 25, SI-1001 Ljubljana, Slovenia
Venue:
Image and Vision Computing
Year:
2008

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Abstract

Learning is a fundamental capability of any cognitive system. To enable efficient operation of a cognitive agent in a real-world environment, visual learning has to be a continuous and robust process. In this article, we present a method for subspace learning, which takes these considerations into account. We present an incremental method, which sequentially updates the principal subspace considering weighted influence of individual images as well as individual pixels within an image. We further extend this approach to enable determination of consistencies in the input data and imputation of the inconsistent values using the previously acquired knowledge, resulting in a novel method for incremental, weighted, and robust subspace learning. We demonstrate the effectiveness of the proposed concept in several experiments on learning of object and background representations.