Abstract
Most of texture classification techniques are evaluated using large rectangular samples of each texture. This, however, is unrealistic, especially as samples of a certain texture may be used as cues in searching image databases or identifying objects in a scene. In this paper, four different texture classification methods (wavelets, cooccurrence matrices, sum and difference histograms, and 1D Boolean models) are systematically compared and evaluated with respect to their performance in identifying textures from small and irregular samples.