Boosting Image Database Retrieval
We present an approach for image database retrieval using a very large number of highly-selective features and simple on-line learning. Our approach is predicated on the assumption that each image is generated by a sparse set of visual "causes" and that images which are visually simil...
| Main Authors: | , |
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| Language: | en_US |
| Published: |
2004
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/5927 |
| _version_ | 1826194807534387200 |
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| author | Tieu, Kinh Viola, Paul |
| author_facet | Tieu, Kinh Viola, Paul |
| author_sort | Tieu, Kinh |
| collection | MIT |
| description | We present an approach for image database retrieval using a very large number of highly-selective features and simple on-line learning. Our approach is predicated on the assumption that each image is generated by a sparse set of visual "causes" and that images which are visually similar share causes. We propose a mechanism for generating a large number of complex features which capture some aspects of this causal structure. Boosting is used to learn simple and efficient classifiers in this complex feature space. Finally we will describe a practical implementation of our retrieval system on a database of 3000 images. |
| first_indexed | 2024-09-23T10:02:06Z |
| id | mit-1721.1/5927 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:02:06Z |
| publishDate | 2004 |
| record_format | dspace |
| spelling | mit-1721.1/59272019-04-12T08:28:13Z Boosting Image Database Retrieval Tieu, Kinh Viola, Paul AI MIT Artificial Intelligence Computer Vision sImage Databases Learning Pattern Matching We present an approach for image database retrieval using a very large number of highly-selective features and simple on-line learning. Our approach is predicated on the assumption that each image is generated by a sparse set of visual "causes" and that images which are visually similar share causes. We propose a mechanism for generating a large number of complex features which capture some aspects of this causal structure. Boosting is used to learn simple and efficient classifiers in this complex feature space. Finally we will describe a practical implementation of our retrieval system on a database of 3000 images. 2004-10-04T14:15:20Z 2004-10-04T14:15:20Z 1999-09-10 AIM-1669 http://hdl.handle.net/1721.1/5927 en_US AIM-1669 7 p. 10275632 bytes 771855 bytes application/postscript application/pdf application/postscript application/pdf |
| spellingShingle | AI MIT Artificial Intelligence Computer Vision sImage Databases Learning Pattern Matching Tieu, Kinh Viola, Paul Boosting Image Database Retrieval |
| title | Boosting Image Database Retrieval |
| title_full | Boosting Image Database Retrieval |
| title_fullStr | Boosting Image Database Retrieval |
| title_full_unstemmed | Boosting Image Database Retrieval |
| title_short | Boosting Image Database Retrieval |
| title_sort | boosting image database retrieval |
| topic | AI MIT Artificial Intelligence Computer Vision sImage Databases Learning Pattern Matching |
| url | http://hdl.handle.net/1721.1/5927 |
| work_keys_str_mv | AT tieukinh boostingimagedatabaseretrieval AT violapaul boostingimagedatabaseretrieval |