Pattern Recognition, International Conference on (2002)
Quebec City, QC, Canada
Aug. 11, 2002 to Aug. 15, 2002
Antonio Robles-Kelly , University of York
Sudeep Sarkar , University of South Florida
Edwin R. Hancock , University of York
We present a fast non-iterative method for approximating the leading eigenvector so as to render graph-spectral based grouping algorithms more efficient. The approximation is based on a linear perturbation analysis and applies to matrices that are non-sparse, non-negative and symmetric. For an N × N matrix, the approximation can be implemented with complexity as low as 0(4N<sup>2</sup>). We provide a performance analysis and demonstrate the usefulness of our method on image segmentation problems.
A. Robles-Kelly, S. Sarkar and E. R. Hancock, "A Fast Leading Eigenvector Approximation for Segmentation and Grouping," Pattern Recognition, International Conference on(ICPR), Quebec City, QC, Canada, 2002, pp. 20639.