Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Descriptive visual words and visual phrases for image applications
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A Comparative Study of Mobile-Based Landmark Recognition Techniques
IEEE Intelligent Systems
City-scale landmark identification on mobile devices
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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In recent years, the Scalable Vocabulary Tree (SVT) has been shown to be effective in image recognition. However, in mobile landmark image recognition where the foreground is the landmark to be recognized while the background is cluttered, the current SVT framework ignores different local importance of image, hence restricting its performance. In this paper, we propose a new landmark recognition framework that can incorporate saliency information to improve the recognition performance relative to the baseline SVT method. Specifically, the saliency information is incorporated in three phases: image descriptor calculation, vocabulary tree generation, and image representation. We constructed a city-scale landmark dataset in Singapore, and the experimental results show that the proposed mobile landmark recognition by incorporating saliency information outperforms the baseline SVT recognition by about 9%.