Please use this identifier to cite or link to this item:
http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6705Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.creator | Bouvrie, Jake V. | - |
| dc.date | 2004-10-08T20:38:35Z | - |
| dc.date | 2004-10-08T20:38:35Z | - |
| dc.date | 2002-12-01 | - |
| dc.date.accessioned | 2013-10-09T02:46:30Z | - |
| dc.date.available | 2013-10-09T02:46:30Z | - |
| dc.date.issued | 2013-10-09 | - |
| dc.identifier | AIM-2002-022 | - |
| dc.identifier | http://hdl.handle.net/1721.1/6705 | - |
| dc.identifier.uri | http://koha.mediu.edu.my:8181/xmlui/handle/1721 | - |
| dc.description | 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. | - |
| dc.format | 1054982 bytes | - |
| dc.format | 824527 bytes | - |
| dc.format | application/postscript | - |
| dc.format | application/pdf | - |
| dc.language | en_US | - |
| dc.relation | AIM-2002-022 | - |
| dc.subject | AI | - |
| dc.title | Multiple Resolution Image Classification | - |
| Appears in Collections: | MIT Items | |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.