TY - GEN
T1 - Joint dictionary learning and topic modeling for image clustering
AU - Li, Lingbo
AU - Zhou, Mingyuan
AU - Wang, Eric
AU - Carin, Lawrence
N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09
PY - 2011/8/18
Y1 - 2011/8/18
N2 - A new Bayesian model is proposed, integrating dictionary learning and topic modeling into a unified framework. The model is applied to cluster multiple images, and a subset of the images may be annotated. Example results are presented on the MNIST digit data and on the Microsoft MSRC multi-scene image data. These results reveal the working mechanisms of the model and demonstrate state-of-the-art performance. © 2011 IEEE.
AB - A new Bayesian model is proposed, integrating dictionary learning and topic modeling into a unified framework. The model is applied to cluster multiple images, and a subset of the images may be annotated. Example results are presented on the MNIST digit data and on the Microsoft MSRC multi-scene image data. These results reveal the working mechanisms of the model and demonstrate state-of-the-art performance. © 2011 IEEE.
UR - http://ieeexplore.ieee.org/document/5946757/
UR - http://www.scopus.com/inward/record.url?scp=80051603758&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2011.5946757
DO - 10.1109/ICASSP.2011.5946757
M3 - Conference contribution
SN - 9781457705397
SP - 2168
EP - 2171
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ER -