Nonnegative Matrix Factorizations Performing Object Detection and Localization

We study the problem of detecting and localizing objects in still, gray-scale images making use of the part-based representation provided by non-negative matrix factorizations. Non-negative 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 non-negative factorization algorithms, which differ in the additional properties added to the non-negative representation of data, in order to investigate if any additional constraint produces better results in general object detection via non-negative matrix factorizations.