Approximation algorithms
Proceedings of the 15th international conference on Multimedia
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 18th international conference on World wide web
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Visual summaries of popular landmarks from community photo collections
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Retrieving landmark and non-landmark images from community photo collections
Proceedings of the international conference on Multimedia
Augmenting mobile city-view image retrieval with context-rich user-contributed photos
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Inferring photographic location using geotagged web images
Multimedia Tools and Applications
City-scale landmark identification on mobile devices
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Proceedings of the 20th ACM international conference on Multimedia
Discovering areas of interest with geo-tagged images and check-ins
Proceedings of the 20th ACM international conference on Multimedia
Probabilistic sequential POIs recommendation via check-in data
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Learning to rank for spatiotemporal search
Proceedings of the sixth ACM international conference on Web search and data mining
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"What is this" and "where am I" are two of the most common questions that arise when people travel abroad. Recently, landmark image retrieval has shown great promise for the addressed problem, where most common approaches are either visual-based or location-based. However, for city-view image retrieval, there could be a number of buildings in a close proximity. Moreover, it is common that photos are taken indoors. The former may degrade the performance of location-based approaches, while the latter may degrade the performance of both the visual-based and location-based approaches. To remedy the deficiencies, this paper further considers the use of check-in data of photos and presents a simple approach that unifies visual features, geo-tags, and check-in data for city-view image retrieval. Furthermore, sparse coding is applied for a high-performance and memory-efficient retrieval implementation, where a graph-based dictionary learning approach is proposed. Experimental results show the effectiveness of the proposed approaches.