Performance evaluation of texture measures with classification based on Kullback discrimination of distributions
Timo Ojala, Matti Pietikäinen, David Harwood · 2002
This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented.