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Invariant Texture Classification Using Group Averaging with Relational Kernel Functions
 

Summary: Invariant Texture Classification Using Group
Averaging with Relational Kernel Functions
Marc Schael
Abstract--In this paper we propose a novel method for the
construction of textural features which are invariant with re-
spect to 2D Euclidean motion and strictly increasing grey
scale transformations. Our approach is based on a group
averaging technique with relational kernel functions. In or-
der to allow for comparison the evaluation of our approach
was done on two image data sets taken from the Brodatz
album. When used for texture classification our technique
compares favourably with existing techniques: error rates
are better. The experimental results reveal that the combi-
nation of both invariance properties leads to highly discrim-
inative and robust textural features.
Keywords-- invariant textural features, texture classifica-
tion, group averaging, relational kernel functions, grey scale
invariance, rotation invariance
I. Introduction
AMONG basic image primitives, e. g. edges, texture is

  

Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung

 

Collections: Computer Technologies and Information Sciences