Abstract
There are millions of mobile phone applications based on location. Using a photo to precisely locate users location is useful and necessary. However, real-time location recognition or retrieval system is a challenging problem due to the really big differences between the query and the dataset in scale, viewpoint and lighting, or the noise existed in the foreground or background etc. To address this problem, we design a place recognition system and a new famous buildings dataset with ground truth labels. By adding a fast geometric image matching procedure before using RANSAC and applying a relative camera orientation calculation algorithm to filter the dataset collected from the Internet, we can substantially improve the efficiency of spatial verification and recognition accuracy.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Wang, Z., Sun, L., Yang, S.: Efficient relative camera orientation detection for mobile applications. In: Proceedings of the 1st International Workshop on Mobile Location-Based Service, pp. 53–62. ACM (2011)
Jacobs, N., Roman, N., Pless, R.: Toward fully automatic geo-location and geo-orientation of static outdoor cameras. In: IEEE Workshop on Applications of Computer Vision, WACV 2008, pp. 1–6. IEEE (2008)
Jégou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. International Journal of Computer Vision 87(3), 316–336 (2010)
Tsai, S., Chen, D., Takacs, G., Chandrasekhar, V., Vedantham, R., Grzeszczuk, R., Girod, B.: Fast geometric re-ranking for image-based retrieval. In: 17th IEEE International Conference on Image Processing (ICIP), pp. 1029–1032. IEEE (2010)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Zisserman, A., Arandjelovic, R.: Three things everyone should know to improve object retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2911–2918. IEEE (2012)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE (2007)
Quack, T., Leibe, B., Van Gool, L.: World-scale mining of objects and events from community photo collections. In: Proceedings of the 2008 International Conference on Content-Based Image and Video Retrieval, pp. 47–56. ACM (2008)
Chum, O., Mikulik, A., Perdoch, M., Matas, J.: Total recall ii: Query expansion revisited. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 889–896. IEEE (2011)
Chum, O., Philbin, J., Sivic, J., Isard, M., Zisserman, A.: Total recall: Automatic query expansion with a generative feature model for object retrieval. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE (2007)
Knopp, J., Sivic, J., Pajdla, T.: Avoiding Confusing Features in Place Recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 748–761. Springer, Heidelberg (2010)
Zobel, J., Moffat, A.: Inverted files for text search engines. ACM Computing Surveys (CSUR) 38(2), 6 (2006)
Chum, O., Matas, J., Obdrzalek, S.: Enhancing ransac by generalized model optimization. In: Proc. of the ACCV, vol. 2, pp. 812–817 (2004)
Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)
Snavely, N., Seitz, S., Szeliski, R.: Modeling the world from internet photo collections. International Journal of Computer Vision 80(2), 189–210 (2008)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
Jégou, H., Douze, M., Schmid, C.: On the burstiness of visual elements. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1169–1176. IEEE (2009)
Jégou, H., Douze, M., Schmid, C.: Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 304–317. Springer, Heidelberg (2008)
Hartley, R.: In defense of the eight-point algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(6), 580–593 (1997)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63–86 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gong, M., Sun, L., Yang, S., Yang, Y. (2012). Robust Place Recognition by Avoiding Confusing Features and Fast Geometric Re-ranking. In: Hu, SM., Martin, R.R. (eds) Computational Visual Media. CVM 2012. Lecture Notes in Computer Science, vol 7633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34263-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-34263-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34262-2
Online ISBN: 978-3-642-34263-9
eBook Packages: Computer ScienceComputer Science (R0)