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|a (NLM)15573825
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|a DE-627
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|e rakwb
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|a eng
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|a Fredembach, Clément
|e verfasserin
|4 aut
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|a Eigenregions for image classification
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|c 2004
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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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|a Band
|b nc
|2 rdacarrier
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|a Date Completed 12.01.2005
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|a Date Revised 02.12.2004
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a For certain databases and classification tasks, analyzing images based region features instead of image features results in more accurate classifications. We introduce eigenregions, which are geometrical features that encompass area, location, and shape properties of an image region, even if the region is spatially incoherent. Eigenregions are calculated using principal component analysis (PCA). On a database of 77,000 different regions obtained through the segmentation of 13,500 real-scene photographic images taken by nonprofessionals, eigenregions improved the detection of localized image classes by a noticeable amount. Additionally, eigenregions allow us to prove that the largest variance in natural image region geometry is due to its area and not to shape or position
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|a Journal Article
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|a Schröder, Michael
|e verfasserin
|4 aut
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|a Süsstrunk, Sabine
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 26(2004), 12 vom: 13. Dez., Seite 1645-9
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:26
|g year:2004
|g number:12
|g day:13
|g month:12
|g pages:1645-9
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|d 26
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