Max-margin Latent Dirichlet Allocation for Image Classification and Annotation

We present the max-margin latent Dirichlet allocation, a max-margin variant of supervised topic models, for image classification and annotation. Our model for image classification (called MMLDA c ) integrates discriminative classification with generative topic models. Our model for image annotation (called MMLDA a ) extends MMLDA c to the case of multi-label problems, where each image can be associated with more than one annotation terms. We derive efficient learning algorithms for both models and demonstrate experimentally the advantages of our proposed models over other baseline methods.