Abstract
This paper investigates how social images and image change detection techniques can be applied to identify the damages caused by natural disasters for disaster assessment. We propose a framework that takes advantages of near duplicate image detection and robust boundary matching for the change detection in disasters. First we perform the near duplicate detection by local interest point-based matching over image pairs. Then, we propose a novel boundary representation model called relative position annulus (RPA), which is robust to boundary rotation, location shift and editing operations. A new RPA matching method is proposed by extending dynamic time wrapping (DTW) from time to position annulus. We have done extensive experiments to evaluate the high effectiveness and efficiency of our approach.
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Kito, N., Zhou, X., Qin, D., Ren, Y., Zhang, X., Thom, J. (2017). Change Detection from Media Sharing Community. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_30
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DOI: https://doi.org/10.1007/978-3-319-63579-8_30
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