Tangent bundle Manifold Learning for image analysis

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6 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationSixth International Conference on Machine Vision, ICMV 2013
PublisherSPIE
ISBN (Print)9780819499967
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event6th International Conference on Machine Vision, ICMV 2013 - London, United Kingdom
Duration: 16 Nov 201317 Nov 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9067
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference6th International Conference on Machine Vision, ICMV 2013
Country/TerritoryUnited Kingdom
CityLondon
Period16/11/1317/11/13

Keywords

  • Data manifold
  • Image manifold
  • Manifold Learning
  • Tangent Bundle Manifold Learning
  • Tangent spaces

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