TY - GEN
T1 - Hierarchical kernel stick-breaking process for multi-task image analysis
AU - An, Qi
AU - Wang, Chunping
AU - Shterev, Ivo
AU - Wang, Eric
AU - Carin, Lawrence
AU - Dunson, David B.
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2008/1/1
Y1 - 2008/1/1
N2 - 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).
AB - 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).
UR - http://portal.acm.org/citation.cfm?doid=1390156.1390159
UR - http://www.scopus.com/inward/record.url?scp=56449106590&partnerID=8YFLogxK
U2 - 10.1145/1390156.1390159
DO - 10.1145/1390156.1390159
M3 - Conference contribution
SN - 9781605582054
SP - 17
EP - 24
BT - Proceedings of the 25th International Conference on Machine Learning
PB - Association for Computing Machinery (ACM)
ER -