https://scholars.lib.ntu.edu.tw/handle/123456789/413020
Title: | City-view image location identification by multiple geo-social media and graph-based image cluster refinement | Authors: | Lee W.-Y. Kuo Y.-H. Hsu W.H. Aizawa K. |
Keywords: | Check-in data; Geo-tagged image; Image location identification; Social media; Sparse coding | Issue Date: | 2016 | Journal Volume: | 41 | Start page/Pages: | 200-211 | Source: | Journal of Visual Communication and Image Representation | Abstract: | ��What is this�� and ��where am I�� are two common questions that arise when people travel abroad. Recently, landmark image identification has shown great promise for the addressed problems, where most previous approaches are either visual-based or location-based. However, regarding city-view image location identification, there could be a number of buildings in a close proximity. Moreover, it is common that photos were taken indoors. The conditions may degrade the performance of previous approaches. To remedy the deficiencies, this paper unifies visual features, geo-tags, and check-in data, based on cross-domain social media, for city-view image location identification. Besides, this paper shows an effective and memory-efficient implementation based on sparse coding, where a new dictionary selection approach is presented. Further, this paper proposes a location-aware graph-based regrouping approach, leveraging spanning graph construction, on clusters of photos to refine clustering results. Experimental results show the improvement over the baselines (location-based, visual-based, etc.). ? 2016 Elsevier Inc. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413020 | ISSN: | 10473203 | DOI: | 10.1016/j.jvcir.2016.09.017 |
Appears in Collections: | 資訊工程學系 |
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