A new type of texture feature based on association rules is proposed in thi
s paper. Association rules have been used in applications such as market ba
sket analysis to capture relationships present among items in large data se
ts. It is shown that association rules can be adapted to capture frequently
occurring local structures in images. Association rules capture both struc
tural and statistical information, and automatically identifies the structu
res that occur most frequently and relationships that have significant disc
riminative power. Methods for classification and segmentation of textured i
mages using association rules as texture features are described. Simulation
results using images consisting of man made and natural textures show that
association rule features perform well compared to other widely used textu
re features. It is shown that association rule features can distinguish tex
ture pairs with identical first, second, and third order statistics, and te
xture pairs that are not easily discriminable visually.