Invariant Texture Classification Using Group Averaging with Relational Kernel Functions
نویسنده
چکیده
In this paper we propose a novel method for the construction of textural features which are invariant with respect to 2D Euclidean motion and strictly increasing grey scale transformations. Our approach is based on a group averaging technique with relational kernel functions. In order 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 combination of both invariance properties leads to highly discriminative and robust textural features. Keywords— invariant textural features, texture classification, group averaging, relational kernel functions, grey scale invariance, rotation invariance
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