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|a (NLM)15460288
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Liu, Xiuwen
|e verfasserin
|4 aut
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| 245 |
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|a Optimal linear representations of images for object recognition
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| 264 |
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|c 2004
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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| 338 |
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 26.10.2004
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|a Date Revised 15.11.2006
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a 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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|a Comparative Study
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|a Journal Article
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| 650 |
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|a Research Support, U.S. Gov't, Non-P.H.S.
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| 700 |
1 |
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|a Srivastava, Anuj
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gallivan, Kyle
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1998
|g 26(2004), 5 vom: 27. Mai, Seite 662-6
|w (DE-627)NLM098212257
|x 0162-8828
|7 nnns
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| 773 |
1 |
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|g volume:26
|g year:2004
|g number:5
|g day:27
|g month:05
|g pages:662-6
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| 952 |
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|d 26
|j 2004
|e 5
|b 27
|c 05
|h 662-6
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