Abstract
Feature matching in omnidirectional vision systems is a challenging problem, mainly because complicated optical systems make the theoretical modelling of invariance and construction of invariant feature descriptors hard or even impossible. In this paper, we propose learning invariant descriptors using a training set of similar and dissimilar descriptor pairs.We use the similarity-preserving hashing framework, in which we are trying to map the descriptor data to the Hamming space preserving the descriptor similarity on the training set. A neural network is used to solve the underlying optimization problem. Our approach outperforms not only straightforward descriptor matching, but also state-of-the-art similarity-preserving hashing methods.
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References
Athitsos, V., Alon, J., Sclaroff, S., Kollios, G.: Boostmap: a method for efficient approximate similarity ranking. In: Proc. CVPR (2004)
Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: SURF: Speeded Up Robust Features. Computer Vision and Image Understanding 10(3), 346–359 (2008)
Bogdanova, I., Bresson, X., Thiran, J.P., Vandergheynst, P.: Scale space analysis and active contours for omnidirectional images. Trans. Image Processing 16(7), 1888–1901 (2007), doi:10.1109/TIP.2007.899008
Bonarini, A., Burgard, W., Fontana, G., Matteucci, M., Sorrenti, D.G., Tardos, J.D.: Rawseeds: Robotics advancement through web-publishing of sensorial and elaborated extensive data sets. In: Proc. IROS Workshop on Benchmarks in Robotics Research (2006), http://www.robot.uji.es/EURON/en/iros06.htm
Briggs, A., Li, Y., Scharstein, D., Wilder, M.: Robot navigation using 1d panoramic images. In: Proc. ICRA (2006)
Bronstein, A., Bronstein, M., Ovsjanikov, M., Guibas, L.: Shape Google: geometric words and expressions for invariant shape retrieval. ACM TOG (2010)
Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Video genome. Tech. Rep. arXiv:1003.5320v1 (2010)
Bronstein, M.M., Bronstein, A.M., Michel, F., Paragios, N.: Data fusion through cross-modality metric learning using similarity-sensitive hashing. In: Proc. CVPR (2010)
Ceriani, S., Fontana, G., Giusti, A., Marzorati, D., Matteucci, M., Migliore, D., Rizzi, D., Sorrenti, D.G., Taddei, P.: Rawseeds ground truth collection systems for indoor self-localization and mapping. Autonomous Robots 27(4), 353–371 (2009), http://www.springerlink.com/content/k924032g72818h53/ , doi:10.1007/s10514-009-9156-5
Cruz, J., Bogdanova, I., Paquier, B., Bierlaire, M., Thiran, J.P.: Scale invariant feature transform on the sphere: Theory and applications. Tech. rep. (2009), http://transp-or2.epfl.ch/technicalReports/CruzBogdPaquBierThir09.pdf
Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. In: European Conference on Computational Learning Theory, pp. 23–37 (1995)
Geyer, C., Stewenius, H.: A nine-point algorithm for estimating para-catadioptric fundamental matrices. In: Proc. CVPR (2007)
Gionis, A., Indik, P., Motwani, R.: Similarity Search in High Dimensions via Hashing. In: International Conference on Very Large Databases (2004)
Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: Proc. CVPR (2006)
Hansen, P.I., Corke, P., Boles, W.: Wide-angle visual feature matching for outdoor localization. Int. J. Robotics Research 29(2/3), 267–297 (2010), http://eprints.qut.edu.au/33736/
Jain, P., Kulis, B., Grauman, K.: Fast image search for learned metrics. In: CVPR (2008)
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., Douze, M., Schmid, C.: Packing Bag-of-Features. In: Proc. ICCV (2009)
Jégou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. Trans. PAMI (2010)
Kimmel, R., Zhang, C., Bronstein, A.M., Bronstein, M.M.: Are mser features really interesting? Trans. PAMI 32(11), 2316–2320 (2011)
Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: Proc. NIPS, pp. 1042–1050 (2009)
Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. IJCV 20(2), 91–110 (2004)
Mauthner, T., Fraundorfer, F., Bischof, H.: Region matching for omnidirectional images using virtual camera planes. Technology (2006)
Mei, C., Rives, P.: Single view point omnidirectional camera calibration from planar grids. In: Proc. ICRA (2007)
Micusik, B., Pajdla, T.: Structure from motion with wide circular field of view cameras. Trans. PAMI 28(7), 1135–1149 (2006), doi:10.1109/TPAMI.2006.151
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. IJCV 65(1/2), 43–72 (2005)
Nayar, S.K.: Catadioptric Omnidirectional Camera. In: Proc. CVPR (1997)
Puig, L., Guerrero, J.J.: Scale space for central catadioptric systems. towards a generic camera feature extractor. In: Proc. ICCV (2011)
Raginsky, M., Lazebnik, S.: Locality-Sensitive Binary Codes from Shift-Invariant Kernels. In: Proc. NIPS (2009)
Salakhutdinov, R., Hinton, G.: Semantic hashing. In: SIGIR Workshop on Information Retrieval and applications of Graphical Models (2007)
Scaramuzza, D., Siegwart, R., Martinelli, A.: A robust descriptor for tracking vertical lines in omnidirectional images and its use in mobile robotics. Int. J. Robotics Research 28(2), 149–171 (2009), http://ijr.sagepub.com/cgi/doi/10.1177/0278364908099858
Schmidhuber, J., Prelinger, D.: Discovering predictable classifications. Neural Computation 5(4), 625–635 (1993)
Shakhnarovich, G.: Learning Task-Specific Similarity. Ph.D. thesis, MIT (2005)
Strecha, C., Bronstein, A.M., Bronstein, M.M., Fua, P.: LDAHash: improved matching with smaller descriptors. Trans. PAMI (2011)
Svoboda, T., Pajdla, T.: Matching in catadioptric images with appropriate windows, and outliers removal. In: Skarbek, W. (ed.) CAIP 2001. LNCS, vol. 2124, pp. 733–740. Springer, Heidelberg (2001)
Taylor, G.W., Spiro, I., Bregler, C., Fergus, R.: Learning invariance through imitation. In: Proc. CVPR (2011)
Tola, E., Lepetit, V., Fua, P.: Daisy: an Efficient Dense Descriptor Applied to Wide Baseline Stereo. Trans. PAMI 32(5), 815–830 (2010)
Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: a large dataset for non-parametric object and scene recognition. Trans. PAMI 30(11), 1958–1970 (2008)
Vedaldi, A.: An open implementation of the SIFT detector and descriptor. Tech. Rep. 070012, UCLA CSD (2007)
Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval. In: CVPR (2010)
Wang, J., Kumar, S., Chang, S.F.: Sequential projection learning for hashing with compact codes. In: ICML (2010)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing (2009)
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Masci, J., Migliore, D., Bronstein, M.M., Schmidhuber, J. (2014). Descriptor Learning for Omnidirectional Image Matching. In: Cipolla, R., Battiato, S., Farinella, G. (eds) Registration and Recognition in Images and Videos. Studies in Computational Intelligence, vol 532. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44907-9_3
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