  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.
