Tangent bundle Manifold Learning for image analysis

A. P. Kuleshov; A. V. Bernstein

doi:10.1117/12.2050125

24 December 2013 Tangent bundle Manifold Learning for image analysis

A. P. Kuleshov, A. V. Bernstein

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 906720 (2013) https://doi.org/10.1117/12.2050125
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

Image applications require additional special features of Manifold Learning (ML) methods. To deal with some of such features, we introduce amplification of the ML, called Tangent Bundle ML (TBML), in which proximity is required not only between the original Data manifold and data-based Reconstructed manifold but also between their tangent spaces. We present a new geometrically motivated Grassman and Stiefel Eigenmaps method for the TBML, which also gives a new solution for the ML.

Citation Download Citation

A. P. Kuleshov and A. V. Bernstein "Tangent bundle Manifold Learning for image analysis", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 906720 (24 December 2013); https://doi.org/10.1117/12.2050125

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