Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Integrating Representative and Discriminative Models for Object Category Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Searching the web with mobile images for location recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Object fingerprints for content analysis with applications to street landmark localization
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Selecting representative and distinctive descriptors for efficient landmark recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Context-aware Discriminative Vocabulary Tree Learning for mobile landmark recognition
Digital Signal Processing
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In this paper, we propose a novel method to model outdoor places with compact local descriptors extracted from images taken around geographical places. Region-based and clustering-based methods are used to reduce the number of feature vectors to represent the natural scene images. A Multiple Queries with User Verification (MQUV) scheme is proposed to improve the recognition accuracy and the system reliability. In our application, a mobile phone camera is used to take images around a place and send them back to the server to get relevant information about the place. The MQUV scheme calculates the maximum confidence level of all top 5 matching places and returns the best matching result to the user together with a typical sample image of the recognized place for the user's visual verification. User is suggested to take more images if the system is not confident enough to provide a result. The user can also make one's own decision by visually matching the returned image with the scenery of the place. Experimental results show that the number of feature vectors is significantly reduced with the compact place modeling and the recognition accuracy is improved with the MQUV scheme.