Text Mining for Automatic Image Tagging

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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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9 p.

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Leong, Chee Wee; Mihalcea, Rada, 1974- & Hassan, Samer August 2010.

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This paper is part of the collection entitled: UNT Scholarly Works and was provided by UNT College of Engineering to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 202 times . More information about this paper can be viewed below.

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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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9 p.

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  • Twenty-third Annual International Conference on Computational Linguistics (COLING), 2010, Beijing, China

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UNT Scholarly Works

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  • August 2010

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  • Jan. 31, 2011, 2:01 p.m.

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  • June 20, 2013, 5:39 p.m.

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Leong, Chee Wee; Mihalcea, Rada, 1974- & Hassan, Samer. Text Mining for Automatic Image Tagging, paper, August 2010; (https://digital.library.unt.edu/ark:/67531/metadc31028/: accessed May 22, 2019), University of North Texas Libraries, Digital Library, https://digital.library.unt.edu; crediting UNT College of Engineering.