Adaptive Matrices for Color Texture Classification

Kerstin Bunte*, Ioannis Giotis, Nicolai Petkov, Michael Biehl

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

In this paper we introduce an integrative approach towards color texture classification learned by a supervised framework. Our approach is based on the Generalized Learning Vector Quantization (GLVQ), extended by an adaptive distance measure which is defined in the Fourier domain and 2D Gabor filters. We evaluate the proposed technique on a set of color texture images and compare results with those achieved by methods already existing in the literature. The features learned by GLVQ improve classification accuracy and they generalize much better for evaluation data previously unknown to the system.

Original languageEnglish
Title of host publicationCOMPUTER ANALYSIS OF IMAGES AND PATTERNS: 14TH INTERNATIONAL CONFERENCE, CAIP 2011, PT 2
EditorsP Real, D DiazPernil, H MolinaAbril, A Berciano, W Kropatsch
Place of PublicationBERLIN
PublisherSpringer
Pages489-497
Number of pages9
ISBN (Electronic)9783642236785
ISBN (Print)978-3-642-23677-8
DOIs
Publication statusPublished - 2011
Event14th International Conference on Computer Analysis of Images and Patterns (CAIP) - , Spain
Duration: 29-Aug-201131-Aug-2011

Publication series

NameLecture Notes in Computer Science
PublisherSPRINGER-VERLAG BERLIN
Volume6855
ISSN (Print)0302-9743

Other

Other14th International Conference on Computer Analysis of Images and Patterns (CAIP)
Country/TerritorySpain
Period29/08/201131/08/2011

Keywords

  • adaptive metric
  • Gabor filter
  • color texture analysis
  • classification
  • Learning Vector Quantization
  • ROTATION-INVARIANT
  • GABOR FILTERS
  • FEATURES
  • SEGMENTATION
  • RETRIEVAL
  • SCALE

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