Location recognition using informative feature vocabulary trees

Details Location recognition using informative feature vocabulary trees Location recognition using informative feature vocabulary trees

2011-05-17

2011-05-17

Ehichioya, Fred I (Department: 2156)

Data processing: database and file management or data structures

Database and file access

Record, file, and data search and comparisons

Reexamination Certificate

active

07945576

ABSTRACT:
A location recognition technique that involves using a query image to identify a depicted location is presented. In addition to the query image, there is also a pre-constructed database of features which are associated with images of known locations. The technique matches features derived from the query image to the database features using a specialized vocabulary tree, which is referred to as an informative feature vocabulary tree. The informative feature vocabulary tree is specialized because it was generated using just those database features that have been deemed informative of known locations. The aforementioned matching features are used to identify a known location image that matches the query image. The location associated with that known location image is then deemed to be the location depicted in the query image.

REFERENCES:
patent: 6430312 (2002-08-01), Huang et al.
patent: 6760714 (2004-07-01), Caid et al.
patent: 6813395 (2004-11-01), Kinjo
patent: 7051019 (2006-05-01), Lund et al.
patent: 7099860 (2006-08-01), Liu et al.
patent: 7113944 (2006-09-01), Zhang et al.
patent: 2003/0195883 (2003-10-01), Mojsilovic et al.
patent: 2005/0002562 (2005-01-01), Nakajima et al.
patent: 2005/0271304 (2005-12-01), Retterath et al.
patent: 2006/0133699 (2006-06-01), Widrow et al.
patent: 2007/0143009 (2007-06-01), Nomura et al.
patent: 2007/0214172 (2007-09-01), Nister et al.
Lin et al., Robust Invariant Features for Object Recognition and Mobile Robot Navigation, 2005, IAPR Conference, pp. 1-4.
Li et al., Probabilistic Location Recognition using Reduced Feature Set, 2006, IEEE, pp. 1-6.
Szeliski et al., City-Scale Location Recognition, 2007, IEEE, pp. 1-7.
Beis, J., and D. G. Lowe, Shape indexing using approximate nearest-neighbour search in high-dimensional spaces, Conf. on Comp. Vision and Pattern Recognition, Puerto Rico (1997), pp. 1000-1006.
Brin, S., Near-neighbor search in large metric spaces, Proceedings of the Int'l Conf. on Very Large Databases (VLDB), 1995, pp. 574-584, Zurich, Switzerland.
Cox, I. J., M. L. Miller, T. P. Minka, T. Papathomas and P. N. Yianilos, The Bayesian image retrieval system, PicHunter: Theory, implementation and psychophysical experiments, IEEE Transactions on Image Processing, Jan. 2000, pp. 20-37, vol. 9, No. 1.
Fukunaga, K., and P. M. Narendra, A branch and bound algorithm for computing k-nearest neighbors, IEEE Transactions on Computers, Jul. 1975, pp. 750-753, vol. 24, No. 7.
Gonzalez, T. F., Clustering to minimize the maximum intercluster distance, J. of Theoretical Comp. Science, Jun. 1985, pp. 293-306, vol. 38, No. 2-3.
Hare, J. S., and P. H Lewis, Saliency-based models of image content and their application to auto-annotation by semantic propagation, Proceedings of Multimedia and the Semantic Web / European Semantic Web Conf., 2005.
Li, F., and J. Kosecka, Probabilistic location recognition using reduced feature set, IEEE Int'l Conf. on Robotics and Automation, May 15-19, 2006, pp. 3405-3410.
Lowe, D. G., Object recognition from local scale-invariant features, Proc. of the Int'l Conf. on Comp. Vision, ICCV, 1999, pp. 1150-1157.
Mojsilovic, A., J. Kovacevic, J. Hu, R. J. Safranek, K. Ganapathy, Matching and retrieval based on the vocabulary and grammar of color patterns, IEEE Trans. on Image Processing, Jan. 2000, pp. 38-54, vol. 9, No. 1.
Moore, A. W., The anchors hierarchy: Using the triangle inequality to survive high dimensional data, Proc. of the 16th Conf. on Uncertainty in Artificial Intelliegence, 2000, pp. 397-405.
Nistér, D., and H. Stewénius, Scalable recognition with a vocabulary tree, IEEE Conf. on Comp. Vision and Pattern Recognition, (CVPR), Jun. 2006, pp. 2161-2168, vol. 2.
Robertson, D. and Cipolla, R., An image-based system for urban navigation, Proc. of the 15th British Machine Vision Conf. (BMVC'04), 2004, pp. 819-828, Digital Imaging Research Centre, Kingston University, Kingston-upon-Thames, Surrey.
Shao, H., T. Svoboda, T. Tuytelaars and L. Van Gool, HPAT indexing for fast object/scene recognition based on local appearance, Computer Lecture Notes on Image and Video Retrieval, Jul. 2003, pp. 71-80.
Vasconcelos, N. Lippman, A., Statistical models of video structure for content analysis andcharacterization, IEEE Transactions on Image Processing, Jan. 2000, pp. 3-19, vol. 9, No. 1.
Vidal-Naquet, M., and S. Ullman, Object recognition with informative features and linear classification, Proc. of the Ninth IEEE Int'l Conf. on Computer Vision, 2003, pp. 281-288, vol. 2.
Zhang, W., and J. Kosecka, Image based localization in urban environments, Proc. of the Third Int'l Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06), 2006, pp. 33-40.
Sivic, J., and A. Zisserman, Video Google: A text retrieval approach to object matching in videos, Ninth IEEE Intl Conf. on Computer Vision, (ICCV'03), Oct. 2003, pp. 1470-1477, vol. 2, IEEE Computer Society.

Affiliated with

Also associated with

Rating

Location recognition using informative feature vocabulary trees does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Location recognition using informative feature vocabulary trees, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Location recognition using informative feature vocabulary trees will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-2692435