A revisit of Generative Model for Automatic Image Annotation using Markov Random Fields

Much research effort on Automatic Image Annotation (AIA) has been focused on Generative Model, due to its well formed theory and competitive performance as compared with many well designed and sophisticated methods. However, when considering semantic context for annotation, the model suffers from the weak learning ability. This is mainly due to the lack of parameter setting and appropriate learning strategy for characterizing the semantic context in the traditional generative model. In this paper, we present a new approach based on Multiple Markov Random Fields (MRF) for semantic context modeling and learning. Differing from previous MRF related AIA approach, we explore the optimal parameter estimation and model inference systematically to leverage the learning power of traditional generative model. Specifically, we propose new potential function for site modeling based on generative model and build local graphs for each annotation keyword. The parameter estimation and model inference is performed in local optimal sense. We conduct experiments on commonly used benchmarks. On Corel 5000 images [3], we achieved 0.36 and 0.31 in recall and precision respectively on 263 keywords. This is a very significant improvement over the best reported result of the current state-of-the-art approaches.