Image database retrieval with multiple-instance learning techniques
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2010
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| Online Access: | http://hdl.handle.net/1721.1/50505 |
| _version_ | 1826212522375512064 |
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| author | Yang, Cheng, 1974- |
| author2 | Tomás Lozano-Pérez. |
| author_facet | Tomás Lozano-Pérez. Yang, Cheng, 1974- |
| author_sort | Yang, Cheng, 1974- |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998. |
| first_indexed | 2024-09-23T15:24:38Z |
| format | Thesis |
| id | mit-1721.1/50505 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:24:38Z |
| publishDate | 2010 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/505052020-07-14T22:12:41Z Image database retrieval with multiple-instance learning techniques Yang, Cheng, 1974- Tomás Lozano-Pérez. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998. Includes bibliographical references (p. 81-82). In this thesis, we develop and test an approach to retrieving images from an image database based on content similarity. First, each picture is divided into many overlapping regions. For each region, the sub-picture is filtered and converted into a feature vector. In this way, each picture is represented by a number of different feature vectors. The user selects positive and negative image examples to train the system. During the training, a multiple-instance learning method known as the Diverse Density algorithm is employed to determine which feature vector in each image best represents the user's concept, and which dimensions of the feature vectors are important. The system tries to retrieve images with similar feature vectors from the remainder of the database. A variation of the weighted correlation statistic is used to determine image similarity. The approach is tested on a large database of natural scenes as well as single- and multiple-object images. Comparisons are made against a previous approach, and the effects of tuning various training parameters, as well as that of adjusting algorithmic details, are also studied. by Cheng Yang. S.M. 2010-01-07T20:46:51Z 2010-01-07T20:46:51Z 1998 1998 Thesis http://hdl.handle.net/1721.1/50505 42306274 eng 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 82 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science Yang, Cheng, 1974- Image database retrieval with multiple-instance learning techniques |
| title | Image database retrieval with multiple-instance learning techniques |
| title_full | Image database retrieval with multiple-instance learning techniques |
| title_fullStr | Image database retrieval with multiple-instance learning techniques |
| title_full_unstemmed | Image database retrieval with multiple-instance learning techniques |
| title_short | Image database retrieval with multiple-instance learning techniques |
| title_sort | image database retrieval with multiple instance learning techniques |
| topic | Electrical Engineering and Computer Science |
| url | http://hdl.handle.net/1721.1/50505 |
| work_keys_str_mv | AT yangcheng1974 imagedatabaseretrievalwithmultipleinstancelearningtechniques |