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• W2471456351 abstract "Automatic Image Tagging seeks to assign relevant words (e.g. “jungle”, “boat”, “trees”) to images that describe the actual content found in the images without intermediate manual labelling. Current approaches are largely based on categorization, and treat the tags independently, so an annotation (jungle,trees) is just as plausible as (jungle,snow). The goal of this dissertation was to develop a probabilistic model (the Continuous Relevance Model) to take into account the dependencies between keywords so as to provide more precise annotations. The main findings suggest that, under certain conditions, taking into account keyword correlation, coupled with an efficient method (beam search) to search over sets of tags is an effective method to increase annotation accuracy." @default.
• W2471456351 created "2016-07-22" @default.
• W2471456351 creator A5035887312 @default.
• W2471456351 date "2009-01-01" @default.
• W2471456351 modified "2023-09-26" @default.
• W2471456351 title "Automatic Image Tagging" @default.
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• W2471456351 hasPublicationYear "2009" @default.
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