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).
|Original language||English (US)|
|Title of host publication||Proceedings of the 28th International Conference on Machine Learning, ICML 2011|
|Number of pages||8|
|State||Published - Oct 7 2011|