  Texture characterization is a key problem in image understanding and pattern
recognition. In this paper, we present a flexible shape-based texture
representation using shape co-occurrence patterns. More precisely, texture
images are first represented by tree of shapes, each of which is associated
with several geometrical and radiometric attributes. Then four typical kinds of
shape co-occurrence patterns based on the hierarchical relationship of the
shapes in the tree are learned as codewords. Three different coding methods are
investigated to learn the codewords, with which, any given texture image can be
encoded into a descriptive vector. In contrast with existing works, the
proposed method not only inherits the strong ability to depict geometrical
aspects of textures and the high robustness to variations of imaging conditions
from the shape-based method, but also provides a flexible way to consider shape
relationships and to compute high-order statistics on the tree. To our
knowledge, this is the first time to use co-occurrence patterns of explicit
shapes as a tool for texture analysis. Experiments on various texture datasets
and scene datasets demonstrate the efficiency of the proposed method.
