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Computational Intelligence and Neuroscience
Volume 2008, Article ID 857453, 14 pages
Research Article

Robust Object Recognition under Partial Occlusions Using NMF

Smart systems division, ARC Seibersdorf research GmbH, 2444 Seibersdorf, Austria

Received 2 October 2007; Revised 18 December 2007; Accepted 10 March 2008

Academic Editor: Morten Morup

Copyright © 2008 Daniel Soukup and Ivan Bajla. 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.


In recent years, nonnegative matrix factorization (NMF) methods of a reduced image data representation attracted the attention of computer vision community. These methods are considered as a convenient part-based representation of image data for recognition tasks with occluded objects. A novel modification in NMF recognition tasks is proposed which utilizes the matrix sparseness control introduced by Hoyer. We have analyzed the influence of sparseness on recognition rates (RRs) for various dimensions of subspaces generated for two image databases, ORL face database, and USPS handwritten digit database. We have studied the behavior of four types of distances between a projected unknown image object and feature vectors in NMF subspaces generated for training data. One of these metrics also is a novelty we proposed. In the recognition phase, partial occlusions in the test images have been modeled by putting two randomly large, randomly positioned black rectangles into each test image.