DocumentCode
2159059
Title
Joint dictionary learning and topic modeling for image clustering
Author
Li, Lingbo ; Zhou, Mingyuan ; Wang, Eric ; Carin, Lawrence
Author_Institution
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
fYear
2011
fDate
22-27 May 2011
Firstpage
2168
Lastpage
2171
Abstract
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.
Keywords
belief networks; image processing; learning (artificial intelligence); Bayesian model; MNIST digit data; Microsoft MSRC multiscene image data; image clustering; joint dictionary learning; topic modeling; Bayesian methods; Computational modeling; Computer vision; Dictionaries; Feature extraction; Image coding; Pattern recognition; Bayesian; annotating; dictionary learning; image clustering; sparse coding; topic modeling;
fLanguage
English
Publisher
ieee
Conference_Titel
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location
Prague
ISSN
1520-6149
Print_ISBN
978-1-4577-0538-0
Electronic_ISBN
1520-6149
Type
conf
DOI
10.1109/ICASSP.2011.5946757
Filename
5946757
Link To Document