F. DiMaio & J. Shavlik (2006).
Belief Propagation in Large, Highly Connected Graphs for 3D Part-Based Object Recognition. Proceedings of the Sixth IEEE International Conference on Data Mining (ICDM'06), pp. 845-850, Hong Kong.
A longer version of this paper appears as UW Machine Learning Research Group Working Paper 06-1.
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We describe a part-based object-recognition framework, specialized to mining complex 3D objects from detailed 3D images. Objects are modeled as a collection of parts together with a pairwise potential function. An efficient inference algorithm - based on belief propagation (BP) - finds the optimal layout of parts, given some input image. We introduce AggBP, a message aggregation scheme for BP, in which groups of messages are approximated as a single message. For objects consisting of N parts, we reduce CPU time and memory requirements from O(N^2) to O(N). We apply AggBP on synthetic data as well as a real-world task identifying protein fragments in three-dimensional images. These experiments show that our improvements result in minimal loss in accuracy in significantly less time.
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