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Computational Intelligence and Neuroscience
Volume 2009 (2009), Article ID 381457, 19 pages
Research Article

Unsupervised Learning of Overlapping Image Components Using Divisive Input Modulation

1Division of Engineering, King's College London, London WC2R 2LS, UK
2Centre for Brain and Cognitive Development, Birkbeck College, University of London, London WC1E 7HX, UK
3Artificial Intelligence Group, Institute of Computer Science, Freie Universit├Ąt Berlin, 14195 Berlin, Germany

Received 27 August 2008; Accepted 7 February 2009

Academic Editor: Seungjin Choi

Copyright © 2009 M. W. Spratling et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper demonstrates that nonnegative matrix factorisation is mathematically related to a class of neural networks that employ negative feedback as a mechanism of competition. This observation inspires a novel learning algorithm which we call Divisive Input Modulation (DIM). The proposed algorithm provides a mathematically simple and computationally efficient method for the unsupervised learning of image components, even in conditions where these elementary features overlap considerably. To test the proposed algorithm, a novel artificial task is introduced which is similar to the frequently-used bars problem but employs squares rather than bars to increase the degree of overlap between components. Using this task, we investigate how the proposed method performs on the parsing of artificial images composed of overlapping features, given the correct representation of the individual components; and secondly, we investigate how well it can learn the elementary components from artificial training images. We compare the performance of the proposed algorithm with its predecessors including variations on these algorithms that have produced state-of-the-art performance on the bars problem. The proposed algorithm is more successful than its predecessors in dealing with overlap and occlusion in the artificial task that has been used to assess performance.