International Journal of Computer Vision
A Mobile Vision System for Urban Detection with Informative Local Descriptors
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
International Journal of Computer Vision
Proceedings of the 15th international conference on Multimedia
A Survey on Mobile Landmark Recognition for Information Retrieval
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
A Comparative Study of Mobile-Based Landmark Recognition Techniques
IEEE Intelligent Systems
A multi-scale learning approach for landmark recognition using mobile devices
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
City-scale landmark identification on mobile devices
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Contextual weighting for vocabulary tree based image retrieval
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Recently, mobile landmark recognition has become one of the emerging applications in mobile media, offering landmark information and e-commerce opportunities to both mobile users and business owners. Existing mobile landmark recognition techniques mainly use GPS (Global Positioning System) location information to obtain a shortlist of database landmark images nearby the query image, followed by visual content analysis within the shortlist. This is insufficient since (i) GPS data often has large errors in dense build-up areas, and (ii) direction data that can be acquired from mobile devices is underutilized to further improve recognition. In this paper, we propose to integrate content and context in an effective and efficient vocabulary tree framework. Specifically, visual content and two types of mobile context: location and direction, can be integrated by the proposed Context-aware Discriminative Vocabulary Tree Learning (CDVTL) algorithm. The experimental results show that the proposed mobile landmark recognition method outperforms the state-of-the-art methods by about 6%, 21% and 13% on NTU Landmark-50, PKU Landmark-198 and the large-scale San Francisco landmark dataset, respectively.