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Content Provider | IEEE Xplore Digital Library |
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Author | DeCoste, D. Burl, M.C. |
Copyright Year | 2000 |
Description | Author affiliation: Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA (DeCoste, D.) |
Abstract | This paper presents a new approach for achieving distortion-invariant recognition and classification. A test example to be classified is viewed as a query intended to find similar examples in the training set (or to find similar class models that represent a compression of the training set). The key idea is that instead of querying with a single pattern, we construct a more robust query, based on the family of patterns formed by distorting the test example. Although query execution is slower than if the invariances were successfully pre-compiled during training, there are significant advantages in several contexts: (i) providing invariances in memory-based learning, (ii) in model selection, where reducing training time at the expense of test time is a desirable trade-off, and (iii) in enabling robust, ad hoc searches based on a single example. Preliminary tests for memory-based learning on the NIST handwritten digit database with a limited set of shearing and translation distortions produced an error rate of 1.35%. |
Starting Page | 732 |
Ending Page | 737 |
File Size | 278572 |
Page Count | 6 |
File Format | |
ISBN | 0769506623 |
ISSN | 10636919 |
DOI | 10.1109/CVPR.2000.855893 |
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 | Chromium |
Content Type | Text |
Resource Type | Article |
Subject | Computer Vision and Pattern Recognition Software |
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