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In this paper we introduce a learning approach to improve the efficiency of manual image annotation. Although important in practice, manual image annotation has rarely been studied in a quantitative way. We propose formal models to characterize the annotation times for two commonly used manual annotation approaches, i.e., tagging and browsing. The formal models make clear the complementary properties of these two approaches, and inspire a learning-based hybrid annotation algorithm. Our experiments show that the proposed algorithm can achieve up to a 50% reduction in annotation time over baseline methods.
Date of Conference: 23-28 June 2008