Probabilistic models for generating, modelling and matching image categories

Hayit Greenspan*, Shiri Gordon, Jacob Golberger

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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-to-category matching is investigated and unsupervised clustering of a random image set into visually coherent image categories is demonstrated.

Original languageEnglish
Pages (from-to)970-973
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Issue number3
StatePublished - 2002


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