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|a eng
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| 100 |
1 |
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|a Andreetto, Marco
|e verfasserin
|4 aut
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| 245 |
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|a Unsupervised learning of categorical segments in image collections
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|c 2012
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|a Text
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|a ƒaComputermedien
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|a Date Completed 05.02.2013
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|a Date Revised 29.01.2022
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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 Which one comes first: segmentation or recognition? We propose a unified framework for carrying out the two simultaneously and without supervision. The framework combines a flexible probabilistic model, for representing the shape and appearance of each segment, with the popular “bag of visual words” model for recognition. If applied to a collection of images, our framework can simultaneously discover the segments of each image and the correspondence between such segments, without supervision. Such recurring segments may be thought of as the “parts” of corresponding objects that appear multiple times in the image collection. Thus, the model may be used for learning new categories, detecting/classifying objects, and segmenting images, without using expensive human annotation
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| 650 |
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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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 Zelnik-Manor, Lihi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Perona, Pietro
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 34(2012), 9 vom: 01. Sept., Seite 1842-55
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