Semantically coherent image annotation with a learning-based keyword propagation strategy

Automatic image annotation plays an important role in modern keyword-based image retrieval systems. Recently, many neighbor-based methods have been proposed and achieved good performance for image annotation. However, existing work mainly focused on exploring a distance metric learning algorithm to determine the neighbors of an image, and neglected the subsequent keyword propagation process. They usually used some simple heuristic propagation rules, and propagated each keyword independently without considering the inherent semantic coherence among keywords. In this paper, we propose a novel learning-based keyword propagation strategy and incorporate it into the neighbor-based method framework. In particular, we employ the structural SVM to learn a scoring function which can evaluate different candidate keyword sets for a test image. Moreover, we explicitly enforce the semantic coherence constraint for the propagated keywords in our approach. The annotation of the test image is propagated as a whole rather than separate keywords. Experiments on two benchmark data sets demonstrate the effectiveness of our approach for image annotation and ranked retrieval.