Recognition of Wood Porosity Based on Direction Insensitive Feature Sets

The size and configuration of pores are key features for automatic wood identification. In this paper, two feature sets insensitive to rotation and transition change are extracted and then used for construction of decision trees for recognizing three different kinds of pore distributions in wood microscopic images. The contribution of this paper lies in three aspects. Firstly, two direction insensitive sets of features for porosity classification are designed and extracted, Secondly, for turning the found classification rule into human-readable knowledge, decision trees are built with the feature sets by C4.5 algorithm; Finally, rules extracted from the decision trees are explained according to domain knowledge of