On the integration of topic modeling and dictionary learning

A new nonparametric Bayesian model is developed to integrate dictionary learning and topic model into a unified framework. The model is employed to analyze partially annotated images, with the dictionary learning performed directly on image patches. Efficient inference is performed with a Gibbs-slice sampler, and encouraging results are reported on widely used datasets. Copyright 2011 by the author(s)/owner(s).

Duke Authors

Cited Authors

  • Li, L; Zhou, M; Sapiro, G; Carin, L

Published Date

  • 2011

Published In

  • Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Start / End Page

  • 625 - 632

Citation Source

  • SciVal