PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:3341
Deposited By:Jacob Goldberger
Deposited On:08 February 2008