# Texture Characterization by Using Shape Co-occurrence Patterns

## TL;DR

This paper introduces a novel shape co-occurrence pattern approach for texture characterization, leveraging shape trees and hierarchical relationships to improve robustness and flexibility in texture analysis.

## Contribution

It presents the first use of shape co-occurrence patterns for texture analysis, combining geometrical and radiometric attributes with hierarchical shape relationships.

## Key findings

- Effective texture discrimination demonstrated on multiple datasets.
- Robustness to imaging condition variations shown.
- Flexible shape relationship modeling enhances analysis.

## Abstract

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.