Lee W.-Y.Kuo Y.-H.Hsu W.H.Aizawa K.2019-07-102019-07-10201610473203https://scholars.lib.ntu.edu.tw/handle/123456789/413020��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.Check-in data; Geo-tagged image; Image location identification; Social media; Sparse coding[SDGs]SDG11journal article10.1016/j.jvcir.2016.09.0172-s2.0-84996558198