Affordable Access

Publisher Website

A probabilistic spectral framework for grouping and segmentation

Authors
Journal
Pattern Recognition
0031-3203
Publisher
Elsevier
Publication Date
Volume
37
Issue
7
Identifiers
DOI: 10.1016/j.patcog.2003.10.017
Keywords
  • Graph-Spectral Methods
  • Maximum Likelihood
  • Perceptual Grouping
  • Motion Segmentation
Disciplines
  • Computer Science
  • Mathematics

Abstract

Abstract This paper presents an iterative spectral framework for pairwise clustering and perceptual grouping. Our model is expressed in terms of two sets of parameters. Firstly, there are cluster memberships which represent the affinity of objects to clusters. Secondly, there is a matrix of link weights for pairs of tokens. We adopt a model in which these two sets of variables are governed by a Bernoulli model. We show how the likelihood function resulting from this model may be maximised with respect to both the elements of link-weight matrix and the cluster membership variables. We establish the link between the maximisation of the log-likelihood function and the eigenvectors of the link-weight matrix. This leads us to an algorithm in which we iteratively update the link-weight matrix by repeatedly refining its modal structure. Each iteration of the algorithm is a three-step process. First, we compute a link-weight matrix for each cluster by taking the outer-product of the vectors of current cluster-membership indicators for that cluster. Second, we extract the leading eigenvector from each modal link-weight matrix. Third, we compute a revised link weight matrix by taking the sum of the outer products of the leading eigenvectors of the modal link-weight matrices.

There are no comments yet on this publication. Be the first to share your thoughts.

Statistics

Seen <100 times
0 Comments

More articles like this

An expectation–maximisation framework for segmenta...

on Image and Vision Computing Jan 01, 2002

Combining a deformable model and a probabilistic f...

on International journal of compu... March 2009

A Probabilistic Approach to Perceptual Grouping

on Computer Vision and Image Unde... Jan 01, 1996

Grouping and segmentation in binocular rivalry

on Vision Research Jan 01, 2004
More articles like this..