Boosting Image Database Retrieval (1999)
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author = {Kinh H. Tieu and Paul Viola},
title = {Boosting Image Database Retrieval},
year = {1999}
}
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Abstract:
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. Copyright c # Massachusetts Institute of Technology, 1998. This publication can be retrieved by anonymous ftp at URL ftp://publications.ai.mit.edu/ai-publications/ This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for this research was provided in part by Nippon Telephone and Telegraph under...
Citations
| 1205 | Schapire, “Decision-theoretic generalization of on-line learning and application to boosting – Freund, E - 1997 |
| 1091 | Support-vector network – Cortes, Vapnik - 1995 |
| 451 | The QBIC project: Querying images by content using color, texture and shape – Niblack, Barber - 1994 |
| 383 | Photobook: Content-based manipulation of image databases – Pentland, Picard, et al. - 1996 |
| 78 | Query by image example: The CANDID approach – Kelly, Cannon, et al. - 1995 |
| 30 | Structure driven image database retrieval – Bonet, Viola - 1997 |
| 5 | Corel stock photo images. http://www.corel.com – Corporation |
