Abstract
In this paper we introduce a new class of image models, which we
call dynamic trees or DTs. A dynamic tree model specifies a prior
over a large number of trees, each one of which is a tree-structured
belief net (TSBN). Experiments show that DTs are capable of
generating images that are less blocky, and the models have better
translation invariance properties than a fixed, "balanced" TSBN.
We also show that Simulated Annealing is effective at finding trees
which have high posterior probability.
| Original language | English |
|---|---|
| Title of host publication | Advances in Neural Information Processing Systems 11 (NIPS 1998) |
| Publisher | MIT Press |
| Pages | 634-640 |
| Number of pages | 7 |
| Publication status | Published - 1999 |