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231224s2013 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2013.2259836
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|a eng
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| 100 |
1 |
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|a Guo, Zhenyu
|e verfasserin
|4 aut
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| 245 |
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|a Cross-domain object recognition via input-output kernel analysis
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|c 2013
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|a Text
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|a ƒaComputermedien
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|a Date Completed 08.01.2014
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|a Date Revised 07.06.2013
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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 It is of great importance to investigate the domain adaptation problem of image object recognition, because now image data is available from a variety of source domains. To understand the changes in data distributions across domains, we study both the input and output kernel spaces for cross-domain learning situations, where most labeled training images are from a source domain and testing images are from a different target domain. To address the feature distribution change issue in the reproducing kernel Hilbert space induced by vector-valued functions, we propose a domain adaptive input-output kernel learning (DA-IOKL) algorithm, which simultaneously learns both the input and output kernels with a discriminative vector-valued decision function by reducing the data mismatch and minimizing the structural error. We also extend the proposed method to the cases of having multiple source domains. We examine two cross-domain object recognition benchmark data sets, and the proposed method consistently outperforms the state-of-the-art domain adaptation and multiple kernel learning methods
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Wang, Z Jane
|e verfasserin
|4 aut
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| 773 |
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8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 22(2013), 8 vom: 21. Aug., Seite 3108-19
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|x 1941-0042
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|g year:2013
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|u http://dx.doi.org/10.1109/TIP.2013.2259836
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