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
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;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2011.5946757
