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In this paper, a structured max-margin learning scheme is developed to achieve more effective training of a large number of inter-related classifiers for multi-label image annotation. First, a visual concept network is constructed to characterize the inter-concept visual similarity contexts more precisely and determine the inter-related learning tasks automatically. Second, multiple base kernels are combined to achieve more precise characterization of the diverse visual similarity contextsdoi:10.1145/1816041.1816056 dblp:conf/civr/XueLF10 fatcat:zmhoybpwo5drjlxi577nbyaawq