Supervised information retrieval for text and images
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
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| Format: | Thesis |
| Language: | en_US |
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Massachusetts Institute of Technology
2005
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| Online Access: | http://hdl.handle.net/1721.1/28426 |
| _version_ | 1811083623016693760 |
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| author | Kyriakides, Alexandros, 1977- |
| author2 | Tomaso Poggio. |
| author_facet | Tomaso Poggio. Kyriakides, Alexandros, 1977- |
| author_sort | Kyriakides, Alexandros, 1977- |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. |
| first_indexed | 2024-09-23T12:36:01Z |
| format | Thesis |
| id | mit-1721.1/28426 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:36:01Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/284262019-04-10T22:14:57Z Supervised information retrieval for text and images Kyriakides, Alexandros, 1977- Tomaso Poggio. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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. 2005-09-26T20:23:04Z 2005-09-26T20:23:04Z 2004 2004 Thesis http://hdl.handle.net/1721.1/28426 56993709 en_US M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 74 leaves 2426173 bytes 2433723 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Kyriakides, Alexandros, 1977- Supervised information retrieval for text and images |
| title | Supervised information retrieval for text and images |
| title_full | Supervised information retrieval for text and images |
| title_fullStr | Supervised information retrieval for text and images |
| title_full_unstemmed | Supervised information retrieval for text and images |
| title_short | Supervised information retrieval for text and images |
| title_sort | supervised information retrieval for text and images |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/28426 |
| work_keys_str_mv | AT kyriakidesalexandros1977 supervisedinformationretrievalfortextandimages |