A Fast Leading Eigenvector Approximation for Segmentation and Grouping

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 ? ? ? matrix, the approximation can be implemented with complexity as low as ? ??.We provide a performance analysis and demonstrate the usefulness of our method on image segmentation problems.

• Type: Conference
• Year: 2002
• Where: ICPR
• Authors: Antonio Robles-Kelly, Sudeep Sarkar, Edwin R. Hancock
• Keywords: Computer Vision, Fast Non-iterative Method, ICPR 2002, Linear Perturbation Analysis