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Geometric discriminative features for aerial image retrieval in social media

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Abstract

The aerial image recognition is an important problem in multimedia information retrieval in social media. In this paper, we propose a new approach by integrating aerial image’s local features into a discriminative one which reflects both the geometric property and the color distribution of aerial image. Firstly, each aerial image is segmented into several regions in terms of their color intensities. And region connected graph (RCG), the links between the spatial neighboring regions, is presented to encode the spatial context of aerial images. Secondly, we mine frequent structures in the RCGs corresponding to training aerial images collected from social media. And a set of refined structures are selected among the frequent ones towards being more discriminative and less redundant. Finally, given a new aerial image, its sub-RCGs corresponding to all the refined structures are extracted and quantized into a discriminative feature for aerial image recognition. The experimental results validate the proposed method by providing a more accurate recognition result of the aerial images on different datasets from different social medias.

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Acknowledgments

This paper draws on work supported in part by the following funds: National High Technology Research and Development Program of China (863 Program) under grant number 2011AA010101, National Natural Science Foundation of China under grant number 61002009 and 61304188, Key Science and Technology Program of Zhejiang Province of China under grant number 2012C01035-1, and Zhejiang Provincial Natural Science Foundation of China under grant number LZ13F020004 and LR14F020003, and College Students’ Activity Program in The Innovation of Science and Technology (Program of Xinmiao Talent) of Zhejiang Province under grant number ZX13005002047.

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Authors and Affiliations

  1. Intelligent Transportation and Information Security Lab, Hangzhou Normal University, Hangzhou, Zhejiang, China

    Yingjie Xia, Jinlong Chen & Jun Li

  2. School of Computing, National University of Singapore, Singapore, Singapore

    Ying Zhang

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  1. Yingjie Xia
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  2. Jinlong Chen
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  3. Jun Li
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  4. Ying Zhang
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Correspondence to Yingjie Xia.

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Xia, Y., Chen, J., Li, J. et al. Geometric discriminative features for aerial image retrieval in social media. Multimedia Systems 22, 497–507 (2016). https://doi.org/10.1007/s00530-014-0412-y

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