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A Fast Construction of the Distance Graph Used for the Classification of Heterogeneous Electron Microscopic Projections

  • Miroslaw Kalinowski
  • Alain Daurat
  • Gabor T. Herman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4538)

Abstract

It has been demonstrated that the difficult problem of classifying heterogeneous projection images, similar to those found in 3D electron microscopy (3D-EM) of macromolecules, can be successfully solved by finding an approximate Max k-Cut of an appropriately constructed weighted graph. Despite of the large size (thousands of nodes) of the graph and the theoretical computational complexity of finding even an approximate Max k-Cut, an algorithm has been proposed that finds a good (from the classification perspective) approximate solution within several minutes (running on a standard PC). However, the task of constructing the complete weighted graph (that represents an instance of the projection image classification problems) is computationally expensive. Due to the large number of edges, the computation of edge weights can take tens of hours for graphs containing several thousand nodes. We propose a method, which utilizes an early termination technique, to significantly reduce the computational cost of constructing such graphs. We compare, on synthetic data sets that resemble projection sets encountered in 3D-EM, the performance of our method with that of a brute-force approach and a method based on nearest neighbor search.

Keywords

Neighbor Search Projection Image Distance Graph Near Neighbor Search Fast Construction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Miroslaw Kalinowski
    • 1
  • Alain Daurat
    • 2
  • Gabor T. Herman
    • 1
  1. 1.Department of Computer Science, The Graduate Center City University of New York, 365 Fifth Avenue New York, NY 10016USA
  2. 2.LSIIT CNRS UMR 7005, Université Louis Pasteur (Strasbourg 1), Pôle API, Boulevard Sébastien Brant, 67400 Illkirch-GraffenstadenFrance

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