Abstract
Image-based localization is to provide contextual information based on a query image. Current state-of-the-art methods use 3D Structure-from-Motion reconstruction model to aid in localizing the query image, either by 2D-to-3D matching or by 3D-to-2D matching. By adding camera pose estimation, the system can perform image localization more accurately. However, incorrect feature correspondences between the 2D image and 3D reconstruction remains the main reason for failures in image localization. In our paper, we introduce a new method, which adds features embedding, to reduce the incorrect feature correspondences. We do the query expansion to add correspondences, where the associated 3d point has a high probability to be found in the same camera as the seed set. Using the techniques described, the registration accuracy can be significantly improved. Experiments on several large image datasets have shown our methods to outperform most state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Meier, L., Tanskanen, P., Fraundorfer, F., Pollefeys, M.: Pixhawk: A system for autonomous flight using onboard computer vision. In: ICRA (2011)
Chen, D., Baatz, G., Koser, K., Tsai, S., Vedantham, R., Pylvanainen, T., Roimela, K., Chen, X., Bach, J., Pollefeys, M., Girod, B., Grzeszczuk, R.: City-scale landmark identification on mobile devices. In: CVPR (2011)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60 (2004)
Steinhoff, U., Omerčević, D., Perko, R., Schiele, B., Leonardis, A.: How computer vision can help in outdoor positioning. In: Schiele, B., Dey, A.K., Gellersen, H., de Ruyter, B., Tscheligi, M., Wichert, R., Aarts, E., Buchmann, A.P. (eds.) AmI 2007. LNCS, vol. 4794, pp. 124–141. Springer, Heidelberg (2007)
Robertson, D., Cipolla, R.: An image-based system for urban navigation. In: BMVC (2004)
Zhang, W., Kosecka, J.: Image based localization in urban environments. In: 3DPVT (2006)
Xiao, J., Chen, J., Yeung, D.-Y., Quan, L.: Structuring visual words in 3D for arbitrary-view object localization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 725–737. Springer, Heidelberg (2008)
Irschara, A., Zach, C., Frahm, J., Bischof, H.: From structure-from-motion point clouds to fast location recognition. In: CVPR (2009)
Li, Y., Snavely, N., Huttenlocher, D.P.: Location recognition using prioritized feature matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 791–804. Springer, Heidelberg (2010)
Choudhary, S., Narayanan, P.J.: Visibility probability structure from sfM datasets and applications. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 130–143. Springer, Heidelberg (2012)
Sattler, T., Leibe, B., Kobbelt, L.: Fast image-based localization using direct 2d-to-3d matching. In: ICCV (2011)
Hua, G., Brown, M., Winder, S.: Discriminant embedding for local image descriptors. In: ICCV (2007)
Winder, S., Hua, G., Brown, M.: Picking the best daisy. In: CVPR (2009)
Philbin, J., Isard, M., Sivic, J., Zisserman, A.: Descriptor learning for efficient retrieval. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 677–691. Springer, Heidelberg (2010)
Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: NIPS (2009)
Yagnik, J., Strelow, D., Ross, D., Sung Lin, R.: The power of comparative reasoning. In: ICCV (2011)
Strecha, C., Bronstein, A., Bronstein, M., Fua, P.: Ldahash: Improved matching with smaller descriptors. TPAMI 34 (2012)
Chum, O., Philbin, J., Sivic, J., Isard, M., Zisserman, A.: Total recall: Automatic query expansion with a generative feature model for object retrieval. In: ICCV (2011)
Arandjelovic, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: CVPR (2012)
Philbin, J., Chum, O., Isard, M.J.S., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: CVPR (2007)
Muja, M., Lowe, D.: Fast approximate nearest neighbors with automatic algorithm configuration. In: ICCTPA (2009)
Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24 (1981)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2004) ISBN: 0521540518
Jain, M., Benmokhtar, R., Gros, P., Jegou, H.: Hamming embedding similarity-based image classification. In: ICMR (2012)
Sattler, T., Weyand, T., Leibe, B., Kobbelt, L.: Image retrieval for image-based localization revisited. In: BMVC (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lu, G., Ly, V., Shen, H., Kolagunda, A., Kambhamettu, C. (2013). Improving Image-Based Localization through Increasing Correct Feature Correspondences. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-41914-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41913-3
Online ISBN: 978-3-642-41914-0
eBook Packages: Computer ScienceComputer Science (R0)