Belief Propagation in Large, Highly Connected Graphs for 3D Part-Based Object Recognition

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(N2 ) 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.

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Frank DiMaio, Jude W. Shavlik
• Keywords: Complex 3D Objects, Data Mining, Detailed 3D Images, ICDM 2006, Pairwise Potential Function