SUPERVISED MULTI-MODAL TOPIC MODEL FOR IMAGE ANNOTATION

Multi-modal topic models are probabilistic generative models where hidden topics are learned from data of different types. In this paper we present supervised multi-modal latent Dirichlet allocation (smmLDA), where we incorporate class label (global description) into the joint modeling of visual words and caption words (local description), for image annotation task. We derive variational inference algorithm to approximately compute posterior distribution over latent variables. Experiments on a subset of LabelMe dataset demonstrate the useful behavior of our model, compared to existing topic models.