Adaptive Matrices for Color Texture Classification

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

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

1 Citaat (Scopus)

Samenvatting

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.

Originele taal-2English
TitelCOMPUTER ANALYSIS OF IMAGES AND PATTERNS: 14TH INTERNATIONAL CONFERENCE, CAIP 2011, PT 2
RedacteurenP Real, D DiazPernil, H MolinaAbril, A Berciano, W Kropatsch
Plaats van productieBERLIN
UitgeverijSpringer
Pagina's489-497
Aantal pagina's9
ISBN van elektronische versie9783642236785
ISBN van geprinte versie978-3-642-23677-8
DOI's
StatusPublished - 2011
Evenement14th International Conference on Computer Analysis of Images and Patterns (CAIP) - , Spain
Duur: 29-aug-201131-aug-2011

Publicatie series

NaamLecture Notes in Computer Science
UitgeverijSPRINGER-VERLAG BERLIN
Volume6855
ISSN van geprinte versie0302-9743

Other

Other14th International Conference on Computer Analysis of Images and Patterns (CAIP)
Land/RegioSpain
Periode29/08/201131/08/2011

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