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231224s2014 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2014.2316824
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|a eng
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| 100 |
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|a Qiu, Qiang
|e verfasserin
|4 aut
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|a Information-Theoretic Dictionary Learning for Image Classification
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|c 2014
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 25.11.2015
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|a Date Revised 10.09.2015
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a We present a two-stage approach for learning dictionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, discriminative, and generative. In the first stage, dictionary atoms are selected from an initial dictionary by maximizing the mutual information measure on dictionary compactness, discrimination and reconstruction. In the second stage, the selected dictionary atoms are updated for improved reconstructive and discriminative power using a simple gradient ascent algorithm on mutual information. Experiments using real data sets demonstrate the effectiveness of our approach for image classification tasks
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Patel, Vishal M
|e verfasserin
|4 aut
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| 700 |
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|a Chellappa, Rama
|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 36(2014), 11 vom: 01. Nov., Seite 2173-84
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|x 1939-3539
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|g volume:36
|g year:2014
|g number:11
|g day:01
|g month:11
|g pages:2173-84
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|u http://dx.doi.org/10.1109/TPAMI.2014.2316824
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