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.

• Type: Conference
• Year: 2002
• Where: ICPR
• Authors: Hayit Greenspan, Shiri Gordon, Jacob Goldberger
• Keywords: Coherent Image Categories, Computer Vision, ICPR 2002, Supervised Image Category, Unsupervised Clustering