Texture Analysis with Shape Co-occurrence Patterns

Abstract

This paper presents a flexible shape-based texture analysis method by investigating the co-occurrence patterns of shapes. More precisely, a texture image is represented by a tree of shapes, each of which is associated with several attributes. The modeling of texture is thus converted to characterize the tree of shapes. To this aim, we first learn a set of co-occurrence patterns of shapes from texture images, then establish a bag-of-words model on the learned shape co-occurrence patterns (SCOPs), and finally use the resulting SCOPs distributions as features for texture analysis. In contrast with existing work, 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 texture analysis method, but also provides a more flexible way to model shape relationships (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 of texture retrieval and classification on various databases report state-of-the-art results and demonstrate the efficiency of the proposed method.

A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification

“…Based on our earlier work [36] addressing texture analysis, we further implement an efficient combination of color, texture and structure features for object categorization and scene classification. In this paper, we propose a new color-texture-structure descriptor, referred to as the CTS descriptor, for HRS image classification based on the color binary partition tree (CBPT).…”

“…Texture features reflect a specific, spatially repetitive pattern of surfaces by repeating a particular visual pattern in different spatial positions [35][36][37], e.g., coarseness, contrast and regularity. For HRS images, structure features contain the macroscopic relationships between objects [38][39][40], such as adjacent relations and inclusion relations.…”

“…We design these co-occurrence patterns to analyze the distribution of ground objects. Based on the binary composition structure, we explore the 4 co-occurrence patterns [36] in Table 1. …”

“…The hierarchical structure of the BPT provides multi-scale region representations; thus, the modeling of texture is converted to describe the node (region) of the BPT. The texture description first relies on classical shape moments and then uses the hierarchical structure of the BPT [36].…”

Discriminative Subtree Selection for NBI Endoscopic Image Labeling

“…Then, another texture descriptors based on tree of shapes have been proposed. Liu et al [34] introduced a bag-of-words model of the branches in a tree of shapes and represented co-occurrence patterns of shapes. He et al [35] adopted a basic idea of LBP, and proposed a texture descriptor.…”