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 class models derived from 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%.