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.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/5927 |
| Date | 10 September 1999 |
| Creators | Tieu, Kinh, Viola, Paul |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 7 p., 10275632 bytes, 771855 bytes, application/postscript, application/pdf |
| Relation | AIM-1669 |
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