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On-line Spectral Learning in Exploring 3D Large Scale Geo-Referred Scenes

  • Nikolaos Doulamis
  • Christos Yiakoumettis
  • George Miaoulis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7616)

Abstract

Personalized navigation of 3D large scale geo-referred scenes has a tremendous impact in digital cultural heritage. This is a result of the recent progress in digitization technology which leads to the creation of massive digital geographic libraries. However, an efficient personalized 3D geo-referred architecture requires intelligent and on-line learning strategies able to dynamically capture user’s preferences dynamics. In this paper, we propose an adaptive spectral learning framework towards 3D navigation of geo-referred scenes. Spectral clustering presents advantages compared to traditional center-based partitioning methods, such as the k-means; it effectively categorize non-Gaussian, complex distributions, present invariability to shapes and densities and it does not depend on the similarity metric used since learning is performed through similarity matrices by exploiting pair-wise comparisons. The main difficulty, however, in incorporating spectral learning in a 3D navigation architecture is its static implementation. To handle this difficulty, we propose in this paper an adaptive framework through the use of adaptive spectral learning which tailors 3D navigation to user’s current needs.

Keywords

spectral clustering 3D navigation adaptation personalization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nikolaos Doulamis
    • 1
  • Christos Yiakoumettis
    • 2
  • George Miaoulis
    • 2
  1. 1.Decision Support and Computer Vision Lab.Technical University of CreteChaniaGreece
  2. 2.Technological Educational Insititute of AthensEgaleoGreece

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