Automatic categorization of multimedia documents is an important function for a digital library system. While text categorization has received much attentions by IR researchers, classification of visual data is at its infancy stage. In this paper, we propose a notion of visual keywords for similarity matching between visual contents. Visual keywords can be constructed automatically from samples of visual data through supervised/unsupervised learning. Given a visual content, the occurrences of visual keywords are detected, summarized spatially, and coded via singular value decom-position to arrive at a concise coded description. The methods to create, detect, summarize, select, and code visual keywords will be detailed. Last but not least, we describe an evaluation experiment that classifies professional nature scenery photographs to demonstrate the effectiveness and efficiency of visual keywords for automatic categorization of images in digital libraries.
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