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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3104163
|2 doi
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|a pubmed24n1098.xml
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|a eng
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| 100 |
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|a Bi, Xiuli
|e verfasserin
|4 aut
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| 245 |
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|a 2D-LCoLBP
|b A Learning Two-Dimensional Co-Occurrence Local Binary Pattern for Image Recognition
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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|a Date Completed 23.08.2021
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|a Date Revised 23.08.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a The rotation, scale and translation invariance of extracted features have a high significance in image recognition. Local binary pattern (LBP) and LBP-based descriptors have been widely used in image recognition due to feature discrimination and computational efficiency. However, most of the existing LBP-based descriptors have been designed to achieve rotation invariance while fail to achieve scale invariance. Moreover, it is usually difficult to achieve a good trade-off between the feature discrimination and the feature dimension. In this work, a learning 2D co-occurrence LBP termed 2D-LCoLBP is proposed to address these issues. Firstly, a weighted joint histogram is constructed in different neighborhoods and scales of an image to represent the multi-neighborhood and multi-scale LBP (2D-MLBP) and achieve the rotation invariance. A feature learning strategy is then designed to learn the compact and robust descriptor (2D-LCoLBP) from LBP pattern pairs across different scales in the extracted 2D-MLBP to characterize the most stable local structures and achieve the scale invariance, as well as decrease the feature dimension and improve the noise robustness. Finally, a linear SVM classifier is employed for recognition. We applied the proposed 2D-LCoLBP on four image recognition tasks-texture, object, face and food recognition with ten image databases. Experimental results show that 2D-LCoLBP has obviously low feature dimension but outperforms the state-of-the-art LBP-based descriptors in terms of recognition accuracy under noise-free, Gaussian noise and JPEG compression conditions
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|a Journal Article
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|a Yuan, Yuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xiao, Bin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Weisheng
|e verfasserin
|4 aut
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| 700 |
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|a Gao, Xinbo
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 17., Seite 7228-7240
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g volume:30
|g year:2021
|g day:17
|g pages:7228-7240
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|u http://dx.doi.org/10.1109/TIP.2021.3104163
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