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Dynamic Trees: Learning to Model Outdoor Scenes
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Dynamic Trees: Learning to Model Outdoor Scenes
Nicholas J. Adams7  and Christopher K. I. Williams7
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Institute for Adaptive and Neural Computation, University of Edinburgh, 5 Forrest Hill, Edinburgh, EH1 2QL, UK |
Abstract
This paper considers the dynamic tree (DT) model, first introduced in [ 1]. A dynamic tree specifies a prior over structures of trees, each of which is a forest of one or more tree-structured belief
networks (TSBN). In the literature standard tree-structured belief network models have been found to produce “blocky” segmentations
when naturally occurring boundaries within an image did not coincide with those of the subtrees in the fixed structure of
the network. Dynamic trees have a flexible architecture which allows the structure to vary to create configurations where
the subtree and image boundaries align, and experimentation with the model has shown significant improvements.
Here we derive an EM-style update based upon mean field inference for learning the parameters of the dynamic tree model and
apply it to a database of images of outdoor scenes where all of its parameters are learned. DTs are seen to offer significant
improvement in performance over the fixed-architecture TSBN and in a coding comparison the DT achieves 0.294 bits per pixel
(bpp) compression compared to 0.378 bpp for lossless JPEG on images of 7 colours.
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