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Journal of Electronic Imaging

Geographical topic learning for social images with a deep neural network
Author(s): Jiangfan Feng; Xin Xu
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Paper 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.

Paper Details

Date Published: 29 March 2017
PDF: 9 pages
J. Electron. Imag. 26(2) 023012 doi: 10.1117/1.JEI.26.2.023012
Published in: Journal of Electronic Imaging Volume 26, Issue 2
Show Author Affiliations
Jiangfan Feng, Chongqing Univ. of Posts and Telecommunications (China)
Xin Xu, Chongqing Univ. of Posts and Telecommunications (China)

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