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Fast Keypoint Recognition Using Random Ferns
March 2010 (vol. 32 no. 3)
pp. 448-461
Mustafa Özuysal, Ecole Polytechnique Fédérale de Lausanne, Lausanne
Michael Calonder, Ecole Polytechnique Fédérale de Lausanne, Lausanne
Vincent Lepetit, Ecole Polytechnique Fédérale de Lausanne, Lausanne
Pascal Fua, Ecole Polytechnique Fédérale de Lausanne, Lausanne
While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well as the number of classes grows. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image data sets containing very significant perspective changes.

[1] Y. Amit, 2D Object Detection and Recognition: Models, Algorithms, and Networks. The MIT Press, 2002.
[2] Y. Amit and D. Geman, “Shape Quantization and Recognition with Randomized Trees,” Neural Computation, vol. 9, no. 7, pp.1545-1588, 1997.
[3] J. Beis and D.G. Lowe, “Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1000-1006, 1997.
[4] C.M. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
[5] A. Böcker, S. Derksen, E. Schmidt, A. Teckentrup, and G. Schneider, “A Hierarchical Clustering Approach for Large Compound Libraries,” J. Chemical Information and Modeling, vol. 45, pp. 807-815, 2005.
[6] A. Bosch, A. Zisserman, and X. Munoz, “Image Classification Using Random Forests and Ferns,” Proc. Int'l Conf. Computer Vision, 2007.
[7] M. Calonder, V. Lepetit, and P. Fua, “Keypoint Signatures for Fast Learning and Recognition,” Proc. European Conf. Computer Vision, Oct. 2008.
[8] C. Chow and C. Liu, “Approximating Discrete Probability Distributions with Dependence Trees,” IEEE Trans. Information Theory, vol. 14, no. 3, pp. 462-467, May 1968.
[9] O. Chum and J. Matas, “Matching with PROSAC—Progressive Sample Consensus” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 220-226, June 2005.
[10] A.J. Davison, I. Reid, N. Molton, and O. Stasse, “Monoslam: Real-Time Single Camera Slam,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1052-1067, June 2007.
[11] A.J. Davison, “Real-Time Simultaneous Localisation and Mapping with a Single Camera,” Proc. Int'l Conf. Computer Vision, vol. 2, pp.1403-1410, 2003.
[12] P. Domingos and G. Provan, “On the Optimality of the Simple Bayesian Classifier under Zero-One Loss,” Machine Learning, vol. 29, pp. 103-130, 1997.
[13] L. Fei-Fei, R. Fergus, and P. Perona, “One-Shot Learning of Object Categories,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 594-611, Apr. 2006.
[14] M.A. Fischler and R.C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Comm. ACM, vol. 24, no. 6, pp. 381-395, 1981.
[15] J.H. Friedman and U. Fayyad, “On Bias, Variance, 0/1-Loss, and the Curse-of-Dimensionality,” Data Mining and Knowledge Discovery, vol. 1, pp. 55-77, 1997.
[16] X.-S. Gao, X.-R. Hou, J. Tang, and H.-F. Cheng, “Complete Solution Classification for the Perspective-Three-Point Problem,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 930-943, Aug. 2003.
[17] G.E. Hinton, “Training Products of Experts by Minimizing Contrastive Divergence,” Neural Computation, vol. 14, pp. 1771-1800, 2002.
[18] D. Hoiem, R. Sukthankar, H. Schneiderman, and L. Huston, “Object-Based Image Retrieval Using the Statistical Structure of Images,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 490-497, 2004.
[19] J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, “On Combining Classifiers,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 226-239, Mar. 1998.
[20] V. Lepetit and P. Fua, “Keypoint Recognition Using Randomized Trees,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1465-1479, Sept. 2006.
[21] D. Lowe, Demo Software: Sift Keypoint Detector, http://www.cs.ubc.ca/~lowekeypoints/, 2008.
[22] D.G. Lowe, “Distinctive Image Features from Scale Invariant Keypoints,” Int'l J. Computer Vision, vol. 20, no. 2, pp. 91-110, 2004.
[23] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. Van Gool, “A Comparison of Affine Region Detectors,” Int'l J. Computer Vision, vol. 65, nos. 1/2, pp. 43-72, 2005.
[24] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem,” Proc. Am. Assoc. Artificial Intelligence Nat'l Conf. Artificial Intelligence, 2002.
[25] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, “FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges,” Proc. 16th Int'l Joint Conf. Artificial Intelligence, 2003.
[26] P. Moreels and P. Perona, “Evaluation of Features Detectors and Descriptors Based on 3D Objects,” Int'l J. Computer Vision, vol. 73, 2006. no. 3, pp. 263-284, July 2007.
[27] D. Nister and H. Stewenius, “Scalable Recognition with a Vocabulary Tree,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[28] T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, July 2002.
[29] M. Ozuysal, P. Fua, and V. Lepetit, “Fast Keypoint Recognition in Ten Lines of Code,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2007.
[30] C. Schmid and R. Mohr, “Local Grayvalue Invariants for Image Retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 5, pp. 530-534, May 1997.
[31] J. Sivic and A. Zisserman, “Video Google: Efficient Visual Search of Videos,” Toward Category-Level Object Recognition, pp. 127-144, Springer, 2006.
[32] C. Strecha, R. Fransens, and L. Van Gool, “Wide Baseline Stereo from Multiple Views: A Probabilistic Account,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 552-559, 2004.
[33] C. Strecha, R. Fransens, and L. Van Gool, “Combined Depth and Outlier Estimation in Multi-View Stereo,” Proc. IEEE Conf. Computer Vision and Pattern Recognition 2006.
[34] D. Wagner, G. Reitmayr, A. Mulloni, T. Drummond, and D. Schmalstieg, “Pose Tracking from Natural Features on Mobile Phones,” Proc. Int'l Symp. Mixed and Augmented Reality, Sept. 2008.
[35] B. Williams, G. Klein, and I. Reid, “Real-Time Slam Relocalisation,” Proc. Int'l Conf. Computer Vision, 2007.
[36] F. Zheng and G.I. Webb, “A Comparative Study of Semi-Naive Bayes Methods in Classification Learning,” Proc. Fourth Australasian Data Mining Conf., pp. 141-156, 2005.

Index Terms:
Image processing and computer vision, object recognition, tracking, image registration, feature matching, naive Bayesian.
Citation:
Mustafa Özuysal, Michael Calonder, Vincent Lepetit, Pascal Fua, "Fast Keypoint Recognition Using Random Ferns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 448-461, March 2010, doi:10.1109/TPAMI.2009.23
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