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Geographical topic learning for social images with a deep neural network

[+] Author Affiliations
Jiangfan Feng, Xin Xu

Chongqing University of Posts and Telecommunications, College of Computer Science and Technology, Chongqing, China

J. Electron. Imaging. 26(2), 023012 (Mar 29, 2017). doi:10.1117/1.JEI.26.2.023012
History: Received November 8, 2016; Accepted March 14, 2017
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Abstract.  The use of geographical tagging in social-media images is becoming a part of image metadata and a great interest for geographical information science. It is well recognized that geographical topic learning is crucial for geographical annotation. Existing methods usually exploit geographical characteristics using image preprocessing, pixel-based classification, and feature recognition. How to effectively exploit the high-level semantic feature and underlying correlation among different types of contents is a crucial task for geographical topic learning. Deep learning (DL) has recently demonstrated robust capabilities for image tagging and has been introduced into geoscience. It extracts high-level features computed from a whole image component, where the cluttered background may dominate spatial features in the deep representation. Therefore, a method of spatial-attentional DL for geographical topic learning is provided and we can regard it as a special case of DL combined with various deep networks and tuning tricks. Results demonstrated that the method is discriminative for different types of geographical topic learning. In addition, it outperforms other sequential processing models in a tagging task for a geographical image dataset.

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Citation

Jiangfan Feng and Xin Xu
"Geographical topic learning for social images with a deep neural network", J. Electron. Imaging. 26(2), 023012 (Mar 29, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.2.023012


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