This paper introduces several extractive approaches for automatic image tagging, relying exclusively on information mined from texts. Through evaluations on two datasets, the authors show that their methods exceed competitive baselines by a large margin, and compare favorably with the state-of-the-art that uses both textual and image features.
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This paper introduces several extractive approaches for automatic image tagging, relying exclusively on information mined from texts. Through evaluations on two datasets, the authors show that their methods exceed competitive baselines by a large margin, and compare favorably with the state-of-the-art that uses both textual and image features.
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Leong, Chee Wee; Mihalcea, Rada, 1974- & Hassan, Samer.Text Mining for Automatic Image Tagging,
paper,
August 2010;
(digital.library.unt.edu/ark:/67531/metadc31028/:
accessed September 21, 2017),
University of North Texas Libraries, Digital Library, digital.library.unt.edu;
crediting UNT College of Engineering.