Conference paper
Attribute-based people search in surveillance environments
Daniel A. Vaquero, Rogerio S. Feris, et al.
WACV 2009
This paper introduces a new class of nonlinear feature space transformations in the context of Gaussian Mixture Models. This class of nonlinear transformations is characterized by computationally efficient training algorithms. Experimental results with quadratic feature space transforms are shown to yield modestly improved recognition performance in a speech recognition context. The quadratic feature space transforms are also shown to be beneficial in an adaptation setting.
Daniel A. Vaquero, Rogerio S. Feris, et al.
WACV 2009
Conrad Albrecht, Jannik Schneider, et al.
CVPR 2025
Pavel Kisilev, Daniel Freedman, et al.
ICPR 2012
Michelle X. Zhou, Fei Wang, et al.
ICMEW 2013