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Applied Computational Intelligence and Soft Computing
Volume 2012, Article ID 781987, 19 pages
Research Article

Nonnegative Matrix Factorizations Performing Object Detection and Localization

1Dipartimento di Informatica, Università di Bari, Via E.Orabona 4, I-70125 Bari, Italy
2Dipartimento di Matematica, Università di Bari, Via E. Orabona 4, I-70125 Bari, Italy
3Computer Science and Engineering Ph.D Division, Institute for Advanced Studies Lucca (IMT), Piazza S. Ponziano 6, 55100 Lucca, Italy

Received 18 October 2011; Revised 3 March 2012; Accepted 16 March 2012

Academic Editor: Cezary Z. Janikow

Copyright © 2012 G. Casalino 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.


We study the problem of detecting and localizing objects in still, gray-scale images making use of the part-based representation provided by nonnegative matrix factorizations. Nonnegative matrix factorization represents an emerging example of subspace methods, which is able to extract interpretable parts from a set of template image objects and then to additively use them for describing individual objects. In this paper, we present a prototype system based on some nonnegative factorization algorithms, which differ in the additional properties added to the nonnegative representation of data, in order to investigate if any additional constraint produces better results in general object detection via nonnegative matrix factorizations.