Hierarchical iterative eigendecomposition for motion segmentation

A  Robles-Kelly; A G  Bors; E R  Hancock

Hierarchical iterative eigendecomposition for motion segmentation

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper applies a new clustering approach for identifying and segmenting motion in image sequences. We estimate a matrix whose entries represent similarity probabilities between local motion estimates. We adopt a two step iterative algorithm which consists of a variant of the expectation-maximization algorithm for segmenting regions with similar motion. The proposed algorithm updates cluster memberships in one step while it maximizes the expected log-likelihood in the second step. The performance of the algorithm is improved greatly by the use of modal sharpening.

Original language	English
Title of host publication	2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL II, PROCEEDINGS
Place of Publication	NEW YORK
Publisher	IEEE
Pages	363-366
Number of pages	4
ISBN (Print)	0-7803-6725-1
Publication status	Published - 2001

Keywords

FIELDS

Cite this

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title = "Hierarchical iterative eigendecomposition for motion segmentation",

abstract = "This paper applies a new clustering approach for identifying and segmenting motion in image sequences. We estimate a matrix whose entries represent similarity probabilities between local motion estimates. We adopt a two step iterative algorithm which consists of a variant of the expectation-maximization algorithm for segmenting regions with similar motion. The proposed algorithm updates cluster memberships in one step while it maximizes the expected log-likelihood in the second step. The performance of the algorithm is improved greatly by the use of modal sharpening.",

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author = "A Robles-Kelly and Bors, {A G} and Hancock, {E R}",

year = "2001",

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T1 - Hierarchical iterative eigendecomposition for motion segmentation

AU - Robles-Kelly, A

AU - Bors, A G

AU - Hancock, E R

PY - 2001

Y1 - 2001

N2 - This paper applies a new clustering approach for identifying and segmenting motion in image sequences. We estimate a matrix whose entries represent similarity probabilities between local motion estimates. We adopt a two step iterative algorithm which consists of a variant of the expectation-maximization algorithm for segmenting regions with similar motion. The proposed algorithm updates cluster memberships in one step while it maximizes the expected log-likelihood in the second step. The performance of the algorithm is improved greatly by the use of modal sharpening.

AB - This paper applies a new clustering approach for identifying and segmenting motion in image sequences. We estimate a matrix whose entries represent similarity probabilities between local motion estimates. We adopt a two step iterative algorithm which consists of a variant of the expectation-maximization algorithm for segmenting regions with similar motion. The proposed algorithm updates cluster memberships in one step while it maximizes the expected log-likelihood in the second step. The performance of the algorithm is improved greatly by the use of modal sharpening.

KW - FIELDS

M3 - Conference contribution

SN - 0-7803-6725-1

SP - 363

EP - 366

BT - 2001 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL II, PROCEEDINGS

PB - IEEE

CY - NEW YORK

ER -