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231224s2013 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2013.2246178
|2 doi
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|a eng
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| 100 |
1 |
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|a Chen, Yi-Chen
|e verfasserin
|4 aut
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| 245 |
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|a In-plane rotation and scale invariant clustering using dictionaries
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|c 2013
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 30.12.2013
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|a Date Revised 03.04.2013
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a In this paper, we present an approach that simultaneously clusters images and learns dictionaries from the clusters. The method learns dictionaries and clusters images in the radon transform domain. The main feature of the proposed approach is that it provides both in-plane rotation and scale invariant clustering, which is useful in numerous applications, including content-based image retrieval (CBIR). We demonstrate the effectiveness of our rotation and scale invariant clustering method on a series of CBIR experiments. Experiments are performed on the Smithsonian isolated leaf, Kimia shape, and Brodatz texture datasets. Our method provides both good retrieval performance and greater robustness compared to standard Gabor-based and three state-of-the-art shape-based methods that have similar objectives
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Sastry, Challa S
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Patel, Vishal M
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Phillips, P Jonathon
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chellappa, Rama
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 22(2013), 6 vom: 21. Juni, Seite 2166-80
|w (DE-627)NLM09821456X
|x 1941-0042
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|g volume:22
|g year:2013
|g number:6
|g day:21
|g month:06
|g pages:2166-80
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|u http://dx.doi.org/10.1109/TIP.2013.2246178
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