An efficient two-stage framework for image annotation

Image annotation tasks always lack accuracy and efficiency. Although many techniques that have been proposed in the last decade can give a reasonable performance, the large number of potential labels causes trouble in terms of decreasing the accuracy and efficiency. Both generative models and discriminative models have been proposed to solve the multi-label problem. Most of these complex models fail to achieve a good performance when they face an increasing number of image collections, with a dictionary that covers a large number of potential semantics. In this paper, we present a two-stage method for multi-class image labeling. We first introduce a simple label-filtering algorithm, which can remove most of the irrelevant labels for a query image while the potential labels are maintained. With a small population of potential labels left, we then explore the relationship between the features to be used and each single class. Hence, specific and effective features will be selected for each class to form a label-specific classifier. In other words, our approach prunes specific features for each single label and formalizes the annotation task as a discriminative classification problem. Experiments prove that our two-stage framework can achieve both efficiency and accuracy for image annotation.