An optimal reduced representation of a MoG with applications to medical image database classification
Jacob Goldberger, Hayit Green and Jeremie Dreyfuss
In: CVPR 2007, 18-23 June 2007, Minneapolis, USA.
This work focuses on a general framework for image categorization,
classification and retrieval that may be appropriate for medical
image archives. The proposed methodology is comprised of a
continuous and probabilistic image representation scheme using
Gaussian mixture modeling (MoG) along with information-theoretic
image matching measures (KL). A category model is obtained by
learning a reduced model from all the images in the category. We
propose a novel algorithm for learning a reduced representation of a
MoG, that is based on the Unscented-Transform. The superiority of
the proposed method is validated on both simulation experiments and
categorization of a real medical image database.