A spectral technique for image clustering

Elena Tsomko, Hyoung Joong Kim, Ebroul Izquierdo, Valia Guerra Ones

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Given an image i* and an image database V containing and unknown number of image classes, in this paper we propose a technique for finding the class A of V that contains i*. To solve this (1+x)-class clustering problem a novel spectral "asymmetric" formulation of the problem is introduced: The Asymmetric Cut. It permits the extraction of the required class regardless other classes in the data base. The actual goal is to find a spectral formulation of the (1+x)-class clustering problem and to propose an efficient numerical implementation of the approach for large image database. The proposed method finds a subset A that maximizes the similarities within the chosen cluster but it does not involve affinities or dissimilarities among remaining unknown clusters in the database. Asymmetric cuts seamlessly lead to a spectral representation which can be solved by finding the critical points of the corresponding Rayleigh quotient. Following the underlying spectral theoretical approach the critical points correspond to the eigenvectors of an affinity matrix derived from pair-wise similarities involving information related to a single image i* representing the image class of concern. Selected results from experimental evaluation are presented.

Original languageEnglish
Title of host publication2009 International Conference on Ultra Modern Telecommunications and Workshops
DOIs
Publication statusPublished - 2009
Event2009 International Conference on Ultra Modern Telecommunications and Workshops - St. Petersburg, Russian Federation
Duration: 2009 Oct 122009 Oct 14

Publication series

Name2009 International Conference on Ultra Modern Telecommunications and Workshops

Other

Other2009 International Conference on Ultra Modern Telecommunications and Workshops
CountryRussian Federation
CitySt. Petersburg
Period09/10/1209/10/14

Keywords

  • Asymmetric cut
  • Image classification
  • Normalized cut
  • Spectral clustering

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications

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