2026-09-19T10:34:43Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/284262022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tomaso Poggio.
Kyriakides, Alexandros, 1977-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-26T20:23:04Z
2005-09-26T20:23:04Z
2004
2004
http://hdl.handle.net/1721.1/28426
56993709
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (leaves 73-74).
We present a novel approach to choosing an appropriate image for a news story. Our method uses the caption of the image to retrieve a suitable image. We have developed a word-extraction engine called WordEx. WordEx uses supervised learning to predict which words in the text of a news story are likely to be present in the caption of an appropriate image. The words extracted by WordEx are then used to retrieve the image from a collection of images. On average, the number of words extracted by WordEx is 10% of the original story text. Therefore, this word-extraction engine can also be applied to text documents for feature reduction.
by Alexandros Kyriakides.
M.Eng.
74 leaves
2426173 bytes
2433723 bytes
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Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Supervised information retrieval for text and images
Thesis
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Publication
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<Abstract>We present a novel approach to choosing an appropriate image for a news story. Our method uses the caption of the image to retrieve a suitable image. We have developed a word-extraction engine called WordEx. WordEx uses supervised learning to predict which words in the text of a news story are likely to be present in the caption of an appropriate image. The words extracted by WordEx are then used to retrieve the image from a collection of images. On average, the number of words extracted by WordEx is 10% of the original story text. Therefore, this word-extraction engine can also be applied to text documents for feature reduction.</Abstract>
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