Geographical topic learning for social images with a deep neural network

Jiangfan Feng; Xin Xu

doi:10.1117/1.JEI.26.2.023012

29 March 2017 Geographical topic learning for social images with a deep neural network

Jiangfan Feng, Xin Xu

Author Affiliations +

Funded by: National Nature Science Foundation of China, Natural Science Foundation Project of Chongqing

Journal of Electronic Imaging, Vol. 26, Issue 2, 023012 (March 2017). https://doi.org/10.1117/1.JEI.26.2.023012

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.

Citation Download Citation

Jiangfan Feng and Xin Xu "Geographical topic learning for social images with a deep neural network," Journal of Electronic Imaging 26(2), 023012 (29 March 2017). https://doi.org/10.1117/1.JEI.26.2.023012

Received: 8 November 2016; Accepted: 14 March 2017; Published: 29 March 2017

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KEYWORDS

Neural networks

Visualization

Data modeling

Image classification

Image processing

Feature extraction

Image segmentation

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