Image content annotation using Bayesian framework and complement components analysis

In this paper, we consider image annotation as a problem of image classification, in which each keyword is treated as a distinct class label. We then build a Bayesian model to solve the classification problem. To preserve the in-variation in the training data and reduce the noises, we also propose to estimate the class conditional probabilities in the feature subspace constructed by complement components analysis (CCA). We demonstrate the effectiveness of our approach through experiments in terms of annotation precision and recall.