Binary image classifiction is a problem that has received much attention in recent years. In this paper we evaluate a selection of popular techniques in an effort to find a feature set/ classifier combination which generalizes well to full resolution image data. We then apply that system to images at one-half through one-sixteenth resolution, and consider the corresponding error rates. In addition, we further observe generalization performance as it depends on the number of training images, and lastly, compare the system's best error rates to that of a human performing an identical classification task given teh same set of test images.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6705 |
| Date | 01 December 2002 |
| Creators | Bouvrie, Jake V. |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 1054982 bytes, 824527 bytes, application/postscript, application/pdf |
| Relation | AIM-2002-022 |
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