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