Abstract
Tensor Voting is a local, non parametric method that provides an efficient way to learn the complex geometric manifold structure under a significant amount of outlier noise. The main limitation of the Tensor Voting framework is that it is strictly a local method, thus not efficient to infer the global properties of complex manifolds. We therefore suggest constructing a unique graph which we call the Tensor Voting Graph, in which the affinity is based on the contribution of neighboring points to a point local tangent space estimated by Tensor Voting. The Tensor Voting Graph compactly and effectively represents the global structure of the underlying manifold. We experimentally demonstrate that we can accurately estimate the geodesic distance on complex manifolds, and substantially outperform all state of the art competing approaches, especially when outliers are present. We also demonstrate our method’s superior ability to segment manifolds, first on synthetic data, then on standard data sets for a motion segmentation, with graceful degradation in the presence of noise.
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References
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Coifman, R.R., Lafon, S., Lee, A.B., Maggioni, M., Warner, F., Zucker, S.: Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. In: Proceedings of the National Academy of Sciences, pp. 7426–7431 (2005)
Dijkstra, E.: Communication with an Automatic Computer. Ph.D thesis, University of Amsterdam (1959)
Donoho, D., Grimes, C.: Hessian eigenmaps: Locally linear embedding techniques for high dimensional data. Proceedings of the National Academy of Sciences of the United States of America 100, 5591–5596 (2003)
Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: CVPR, pp. 2790–2797 (2009)
Gong, D., Zhao, X., Medioni, G.: Robust multiple manifold structure learning. In: ICML (2012)
Mordohai, P., Medioni, G.: Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning. Morgan & Claypool Publishers (2006)
Mordohai, P., Medioni, G.: Dimensionality estimation, manifold learning and function approximation using tensor voting. Journal of Machine Learning Research 11, 411–450 (2010)
Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849–856 (2001)
Niyogi, P., Smale, S., Weinberger, S.: Finding the homology of submanifolds with high confidence from random samples. Discrete & Computational Geometry 39(1), 419–441 (2008)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. SCIENCE 290, 2323–2326 (2000)
Singer, A., Wu, H.: Vector diffusion maps and the connection laplacian. Communications on Pure and Applied Mathematics 65(8), 1067–1144 (2012)
Tenenbaum, J., de Silva, V., Langford, J.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290(5500), 2319–2323 (2000)
Vidal, R., Ma, Y., Sastry, S.: Generalized principal component analysis (gpca) (2003)
Wang, Y., Jiang, Y., Wu, Y., Zhou, Z.: Spectral clustering on multiple manifolds. IEEE Transactions on Neural Networks 22(7), 1149–1161 (2011)
Zhang, Z., Zha, H.: Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM Journal on Scientific Computing 26(1), 313–338 (2005)
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Deutsch, S., Medioni, G. (2015). Unsupervised Learning Using the Tensor Voting Graph. In: Aujol, JF., Nikolova, M., Papadakis, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2015. Lecture Notes in Computer Science(), vol 9087. Springer, Cham. https://doi.org/10.1007/978-3-319-18461-6_23
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DOI: https://doi.org/10.1007/978-3-319-18461-6_23
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