Hierarchical Image Probability (H1P) Models

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

Bibtex Metadata Paper


Clay Spence, Lucas Parra


We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exactly into condi(cid:173) tional distributions of feature vectors at one resolution (pyramid level) conditioned on the image information at lower resolutions. We would like to factor this over positions in the pyramid levels to make it tractable, but such factoring may miss long-range dependencies. To fix this, we in(cid:173) troduce hidden class labels at each pixel in the pyramid. The result is a hierarchical mixture of conditional probabilities, similar to a hidden Markov model on a tree. The model parameters can be found with max(cid:173) imum likelihood estimation using the EM algorithm. We have obtained encouraging preliminary results on the problems of detecting various ob(cid:173) jects in SAR images and target recognition in optical aerial images.