This paper presents a novel approach for image annotation with relevance feedback to assist the user in annotating semantic labels for images. Our design for image annotation is based on a semisupervised learning for building hierarchical classifiers associated with annotation labels. We construct individual hierarchical classifiers each corresponding to one semantic label that is used for describing the semantic contents of the images. Our semi-supervised approach for learning classifiers reduces the need of training images by use of both labeled and unlabeled images. We adopt hierarchical approach for classifiers to divide the whole semantic concept associated with a label into several parts such that the complex contents in images can be simplified. We also describe some experiments to show the performance of the proposed approach.
