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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tieu, K. Viola, P. |
| Copyright Year | 2000 |
| Description | Author affiliation: Artificial Intelligence Lab., MIT, Cambridge, MA, USA (Tieu, K.) |
| Abstract | We present an approach for image retrieval using a very large number of highly selective features and efficient online 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 computing a very large number of highly selective features which capture some aspects of this causal structure (in our implementation there are over 45,000 highly selective features). At query time a user selects a few example images, and a technique known as "boosting" is used to learn a classification function in this feature space. By construction, the boosting procedure learns a simple classifier which only relies on 20 of the features. As a result a very large database of images can be scanned rapidly, perhaps a million images per second. Finally we will describe a set of experiments performed using our retrieval system on a database of 3000 images. |
| Starting Page | 228 |
| Ending Page | 235 |
| File Size | 1385577 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769506623 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2000.855824 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-06-15 |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Boosting Image retrieval Image databases Electrical capacitance tomography Spatial databases Humans Artificial intelligence Laboratories Learning Information retrieval |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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