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ICPR
2002
IEEE

Probabilistic Models for Generating, Modelling and Matching Image Categories

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Probabilistic Models for Generating, Modelling and Matching Image Categories
In this paper we present a probabilistic and continuous framework for supervised image category modelling and matching as well as unsupervised clustering of image space into image categories. A generalized GMM-KL framework is described in which each image or image-set (category) is represented as a Gaussian mixture distribution and images (categories) are compared and matched via a probabilistic measure of similarity between distributions. Image-tocategory matching is investigated and unsupervised clustering of a random image set into visually coherent image categories is demonstrated.
Hayit Greenspan, Shiri Gordon, Jacob Goldberger
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2002
Where ICPR
Authors Hayit Greenspan, Shiri Gordon, Jacob Goldberger
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