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Automatic image annotation (AIA) raises tremendous challenges to machinelearning as it requires modeling of data that are both ambiguous in input andoutput, e.g., images containing multiple objects and labeled with multiplesemantic tags. Even more challenging is that the number of candidate tags isusually huge (as large as the vocabulary size) yet each image is only relatedto a few of them. This paper presents a hybrid generative-discriminativeclassifier to simultaneously address the extreme data-ambiguity andoverfitting-vulnerability issues in tasks such as AIA. Particularly: (1) anExponential-Multinomial Mixture (EMM) model is established to capture both theinput and output ambiguity and in the meanwhile to encourage predictionsparsity; and (2) the prediction ability of the EMM model is explicitlymaximized through discriminative learning that integrates variational inferenceof graphical models and the pairwise formulation of ordinal regression.Experiments show that our approach achieves both superior annotationperformance and better tag scalability.

Hybrid Generative/Discriminative Learning for Automatic Image Annotation