Optimal linear representations of images for object recognition
Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gradient algorithm for finding optimal linear representations of images for use in appearance-based object recognition. Using the ne...
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1998. - 26(2004), 5 vom: 27. Mai, Seite 662-6 |
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| Weitere Verfasser: | , |
| Format: | Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2004
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
| Schlagworte: | Comparative Study Journal Article Research Support, U.S. Gov't, Non-P.H.S. |
| Zusammenfassung: | Although linear representations are frequently used in image analysis, their performances are seldom optimal in specific applications. This paper proposes a stochastic gradient algorithm for finding optimal linear representations of images for use in appearance-based object recognition. Using the nearest neighbor classifier, a recognition performance function is specified and linear representations that maximize this performance are sought. For solving this optimization problem on a Grassmann manifold, a stochastic gradient algorithm utilizing intrinsic flows is introduced. Several experimental results are presented to demonstrate this algorithm |
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| Beschreibung: | Date Completed 26.10.2004 Date Revised 15.11.2006 published: Print Citation Status MEDLINE |
| ISSN: | 0162-8828 |