Abstract
Fast keypoint recognition is essential to many vision tasks. In contrast to the classification-based approaches [1,2], we directly formulate the keypoint recognition as an image patch retrieval problem, which enjoys the merit of finding the matched keypoint and its pose simultaneously. A novel convolutional treelets approach is proposed to effectively extract the binary features from the patches. A corresponding sub-signature-based locality sensitive hashing scheme is employed for the fast approximate nearest neighbor search in patch retrieval. Experiments on both synthetic data and real-world images have shown that our method performs better than state-of-the-art descriptor-based and classification-based approaches.
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References
Lepetit, V., Fua, P.: Keypoint recognition using randomized trees. PAMI 28 (2006)
Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. PAMI 32 (2010)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI 27 (2005)
Hinterstoisser, S., Lepetit, V., Benhimane, S., Fua, P., Navab, N.: Learning real-time perspective patch rectification. IJCV 91 (2011)
Zhu, J., Lyu, M.R.: Progressive finite newton approach to real-time nonrigid surface detection. In: Proc. Conf. Computer Vision and Pattern Recognition (2007)
Zhu, J., Lyu, M.R., Huang, T.S.: A fast 2d shape recovery approach by fusing features and appearance. IEEE Trans. Pattern Anal. Mach. Intell. 31, 1210–1224 (2009)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60 (2004)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). CVIU 110 (2008)
Hua, G., Brown, M., Winder, S.: Discriminant embedding for local image descriptors. In: ICCV (2007)
Strecha, C., Bronstein, A., Bronstein, M., Fua, P.: Ldahash: Improved matching with smaller descriptors. PAMI 34 (2011)
Calonder, M., Lepetit, V., Ozuysal, M., Trzinski, T., Strecha, C., Fua, P.: BRIEF: Computing a Local Binary Descriptor Very Fast. PAMI 34 (2011)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: An efficient alternative to sift or surf. In: ICCV (2011)
Lepetit, V., Pilet, J., Fua, P.: Point matching as a classification problem for fast and robust object pose estimation. In: CVPR (2004)
Goedeme, T., Tuytelaars, T., Van Gool, L.: Fast wide baseline matching for visual navigation. In: CVPR (2004)
Rothganger, F., Lazebnik, S., Schmid, C., Ponce, J.: 3d object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. IJCV 66 (2006)
Lee, A.B., Nadler, B., Wasserman, L.: Treelets—an adaptive multi-scale basis for sparse unordered data. Annals of Applied Statistics 2 (2008)
LeCun, Y., Bengio, Y.: The handbook of brain theory and neural networks (1998)
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: VLDB (1999)
Wu, C., Zhu, J., Cai, D., Chen, C., Bu, J.: Semi-supervised nonlinear hashing using bootstrap sequential projection learning. IEEE Transactions on Knowledge and Data Engineering 99 (2012)
Zhang, D., Wang, J., Cai, D., Lu, J.: Self-taught hashing for fast similarity search. In: SIGIR (2010)
Ambai, M., Yoshida, Y.: Card: Compact and real-time descriptors. In: ICCV (2011)
Calonder, M., Lepetit, V., Fua, P.: Keypoint Signatures for Fast Learning and Recognition. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 58–71. Springer, Heidelberg (2008)
Le, Q., Zou, W., Yeung, S., Ng, A.: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: CVPR (2011)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS (2008)
Zhao, W.L., Ngo, C.W., Tan, H.K., Wu, X.: Near-duplicate keyframe identification with interest point matching and pattern learning. IEEE Transactions on Multimedia 9 (2007)
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Wu, C., Zhu, J., Zhang, J., Chen, C., Cai, D. (2012). A Convolutional Treelets Binary Feature Approach to Fast Keypoint Recognition. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7576. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33715-4_27
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DOI: https://doi.org/10.1007/978-3-642-33715-4_27
Publisher Name: Springer, Berlin, Heidelberg
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