Hierarchical kernel stick-breaking process for multi-task image analysis

Journal Article

The kernel stick-breaking process (KSBP) is employed to segment general imagery, imposing the condition that patches (small blocks of pixels) that are spatially proximate are more likely to be associated with the same cluster (segment). The number of clusters is not set a priori and is inferred from the hierarchical Bayesian model. Further, KSBP is integrated with a shared Dirichlet process prior to simultaneously model multiple images, inferring their inter-relationships. This latter application may be useful for sorting and learning relationships between multiple images. The Bayesian inference algorithm is based on a hybrid of variational Bayesian analysis and local sampling. In addition to providing details on the model and associated inference framework, example results are presented for several image-analysis problems. Copyright 2008 by the author(s)/owner(s).

Full Text

Duke Authors

Cited Authors

  • An, Q; Wang, C; Shterev, I; Wang, E; Carin, L; Dunson, DB

Published Date

  • January 1, 2008

Published In

  • Proceedings of the 25th International Conference on Machine Learning

Start / End Page

  • 17 - 24

Digital Object Identifier (DOI)

  • 10.1145/1390156.1390159

Citation Source

  • Scopus