Abstract
Automatic image annotation is crucial for keyword-based image retrieval because it can be used to improve the textual description of images. In this paper, we propose a unified framework for image annotation, which contains two kinds of learning processes and incorporates three kinds of relations among images and keywords. In addition, we propose some improvements on its components, i.e. a reinforced image-to-image relation; a combined word-to-word relation; and a progressive learning method. Experiments on the Corel dataset demonstrate their effectiveness. We also show that many existing image annotation algorithms can be formulated into this framework and present an experimental comparison among these algorithms to evaluate their performance comprehensively.
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