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Pattern Recognition Joint Pattern Recognition Symposium

DAGM 2002: Pattern Recognition pp 498-506 | Cite as

Maximum Entropy and Gaussian Models for Image Object Recognition


The principle of maximum entropy is a powerful framework that can be used to estimate class posterior probabilities for pattern recognition tasks. In this paper, we show how this principle is related to the discriminative training of Gaussian mixture densities using the maximum mutual information criterion. This leads to a relaxation of the constraints on the covariance matrices to be positive (semi-) definite. Thus, we arrive at a conceptually simple model that allows to estimate a large number of free parameters reliably. We compare the proposed method with other state-of-the-art approaches in experiments with the well known US Postal Service handwritten digits recognition task.


Maximum Entropy Covariance Matrice Gaussian Model Neural Information Processing System Relevance Vector Machine





© Springer-Verlag Berlin Heidelberg 2002



Keysers D., Och F.J., Ney H. (2002) Maximum Entropy and Gaussian Models for Image Object Recognition. In: Van Gool L. (eds) Pattern Recognition. DAGM 2002. Lecture Notes in Computer Science, vol 2449. Springer, Berlin, Heidelberg



