Uncertainty in Artificial Intelligence
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Hybrid Generative/Discriminative Learning for Automatic Image Annotation
Shuang Yang, Jiang Bian, Hongyuan Zha
Abstract:
Automatic image annotation (AIA) raises tremendous challenges to machine learning as it requires modeling of data that are both ambiguous in input and output, e.g., images containing multiple objects and labeled with multiple semantic tags. Even more challenging is that the number of candidate tags is usually huge (as large as the vocabulary size) yet each image is only related to a few of them. This paper presents a hybrid generative-discriminative classifier to simultaneously address the extreme data-ambiguity and overfitting-vulnerability issues in tasks such as AIA. Particularly: (1) an Exponential-Multinomial Mixture (EMM) model is established to capture both the input and output ambiguity and in the meanwhile to encourage prediction sparsity; and (2) the prediction ability of the EMM model is explicitly maximized through discriminative learning that integrates variational inference of graphical models and the pairwise formulation of ordinal regression. Experiments show that our approach achieves both superior annotation performance and better tag scalability.
Keywords:
Pages: 683-690
PS Link:
PDF Link: /papers/10/p683-yang.pdf
BibTex:
@INPROCEEDINGS{Yang10,
AUTHOR = "Shuang Yang and Jiang Bian and Hongyuan Zha",
TITLE = "Hybrid Generative/Discriminative Learning for Automatic Image Annotation",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "683--690"
}


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