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A fast leading eigenvector approximation for segmentation and grouping

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Journal16TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL II, PROCEEDINGS
DatePublished - 2002
Number of pages4
Pages (from-to)639-642
Original languageEnglish

Abstract

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 x N matrix, the approximation can be implemented with complexity as low as O(4N(2)). We provide a performance analysis and demonstrate the usefulness of our method on image segmentation problems.

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