Abstract
Place recognition currently suffers from a lack of scalability due to the need for strong geometric constraints, which as of yet are typically limited to RANSAC implementations. In this paper, we present a method to successfully achieve state-of-the-art performance, in both recognition accuracy and speed, without the need for RANSAC. We propose to discretise each feature pair in an image, in both appearance and 2D geometry, to create a triplet of words: one each for the appearance of the two features, and one for the pairwise geometry. This triplet is then passed through an inverted index to find examples of such pairwise configurations in the database. Finally, a global geometry constraint is enforced by considering the maximum-clique in an adjacency graph of pairwise correspondences. The discrete nature of the problem allows for tractable probabilistic scores to be assigned to each correspondence, and the least informative feature pairs can be eliminated from the database for memory and time efficiency. We demonstrate the performance of our method on several large-scale datasets, and show improvements over several baselines.
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Arandjelovic, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2911–2918 (2012)
Cummins, M., Newman, P.: FAB-MAP: Probabilistic localization and mapping in the space of appearance. International Journal of Robotics Research 27, 647–661 (2008)
Heath, K., Gelfand, N., Ovsjanikov, M., Aanjaneya, M., Guibas, L.J.: Image-webs: Computing and exploiting connectivity in image collections. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 3432–3439 (2010)
Jegou, 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)
Jégou, H., Chum, O.: Negative evidences and co-occurrences in image retrieval: The benefit of PCA and whitening. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 774–787. Springer, Heidelberg (2012)
Johns, E., Yang, G.Z.: From images to scenes: Compressing an image cluster into a single scene model for place recognition. In: Proceedings of the IEEE International Conference on Comptuer Vision, pp. 874–881 (2011)
Johns, E., Yang, G.Z.: Generative methods for long-term place recognition in dynamic scenes. pp. 297–314 (2014)
Kalantidis, Y., Tolias, G., Avrithis, Y., Phinikettos, M., Spyrou, E., Mylonas, P., Kollias, S.: VIRaL: Visual image retrieval and localization. Multimedia Tools and Applications 51, 555–591 (2011)
Li, Y., Crandall, D.J., Huttenlocher, D.P.: Landmark classification in large-scale image collections. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1957–1964 (2009)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal on Computer Vision 60, 91–111 (2004)
Mikulík, A., Perdoch, M., Chum, O., Matas, J.: Learning a fine vocabulary. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 1–14. Springer, Heidelberg (2010)
Östergård, P.R.: A fast algorithm for the maximum clique problem. Discrete Appl. Math. 120, 197–201 (2002)
Philbin, J., Chum, O., Isard, M.: J. Sivic, A.Z.: Lost in quantization: Improving particular object retrieval in large scale image databases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Raguram, R., Chum, O., Pollejeys, M., Matas, J., Frahm, J.M.: Usac: A universal framework for random sample consensus. Pattern Analysis and Machine Intelligence 35, 2022–2038 (2013)
Raguram, R., Wu, C., Frahm, J.M., Lazebnik, S.: Modeling and recognition of landmark image collections using iconic scene graphs. International Journal of Computer Vision 95, 213–231 (2011)
Sattler, T., Leibe, B., Kobbelt, L.: Fast image-based localization using direct 2D-to-3D matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 667–674 (2011)
Shen, X., Lin, Z., Brandt, J., Wu, Y.: Spatially-constrained similarity measure for large-scale object retrieval. Pattern Analysis and Machine Intelligence 36, 1229–1241 (2014)
Tolias, G., Kalantidis, Y., Avrithis, Y., Kollias, S.: Towards large-scale geometry indexing by feature selection. Computer Vision and Image Understanding 120(3), 31–45 (2014)
Tolias, G., Avrithis, Y.: Speeded-up, relaxed spatial matching. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1653–1660 (2011)
Wang, R., Tang, Z., Cao, Q.: An efficient approximation algorithm for finding a maximum clique using hopfield network learning. Neural Computing 15(7), 1605–1619 (2003)
Wang, X., Yang, M., Cour, T., Zhu, S., Yu, K., Han, T.X.: Contextual weighting for vocabulary tree based image retrieval. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 209–216 (2011)
Wu, Z., Ke, Q., Isard, M., Sun, J.: Bundling features for large scale partial-duplicate web image search. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 25–32 (2010)
Yuan, U., Wu, Y., Yang, M.: Discovery of collocation patterns: from visual words to visual phrases. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Zhang, Y., Jia, Z., Chen, T.: Image retrieval with geometry-preserving visual phrases. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 809–816 (2011)
Zheng, Y., Zhao, M., Song, Y., Adam, H., Buddemeier, U., Bissacco, A., Brucher, F., Chua, T.S., Neven, H.: Tour the world: Building a web-scale landmark recognition engine. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1085–1092 (2009)
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Johns, E.D., Yang, GZ. (2014). Pairwise Probabilistic Voting: Fast Place Recognition without RANSAC. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8690. Springer, Cham. https://doi.org/10.1007/978-3-319-10605-2_33
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DOI: https://doi.org/10.1007/978-3-319-10605-2_33
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