Characterization of Complex Image Spatial Structures Based on Symmetrical Weibull Distribution Model for Texture Pattern Classification

Jinping Liu,Weihua Gui,Zhaohui Tang,Pengfei Xu,Jiezhou He,Wuxia Zhang

doi:10.1155/2018/6163787

Jinping Liu, Weihua Gui + Show 4 more

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https://doi.org/10.1155/2018/6163787

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Abstract

Texture pattern classification has long been an essential issue in computer vision (CV). However, texture is a kind of perceptual concept of human beings in scene observation or content understanding, which cannot be defined or described clearly in CV. Visually, the visual appearance of the complex spatial structure (CSS) of texture pattern (TP) generally depends on the random organization (or layout) of local homogeneous fragments (LHFs) in the imaged surface. Hence, it is essential to investigate the latent statistical distribution (LSD) behavior of LHFs for distinctive CSS feature characterization to achieve good classification performance. This work presents an image statistical modeling-based TP identification (ISM-TPI) method. It firstly makes a theoretical explanation of the Weibull distribution (WD) behavior of the LHFs of the imaged surface in the imaging process based on the sequential fragmentation theory (SFT), which consequently derives a symmetrical WD model (SWDM) to characterize the LSD of the TP’s SS. Multidirectional and multiscale TP features are then characterized by the SWDM parameters based on the oriented differential operators; in other words, texture images are convolved with multiscale and multidirectional Gaussian derivative filters (GDFs), including the steerable isotropic GDFs (SIGDFs) and the oriented anisotropic GDFs (OAGDFs), for the omnidirectional and multiscale SS detail exhibition with low computational complexity. Finally, SWDM-based TP feature parameters, demonstrated to be directly related to the human vision perception system with significant physical perception meaning, are extracted and used to TP classification with a partial least squares-discriminant analysis- (PLS-DA-) based classifier. The effectiveness of the proposed ISM-TPI method is verified by extensive experiments on three texture image databases. The classification results demonstrate the superiority of the proposed methods over several state-of-the-art TP classification methods.

Highlights

Texture is an important visual perception cue ubiquitously existing in almost all-natural images
The symmetrical WD model (SWDM)-based texture pattern (TP) feature extraction results are directly related to the SS characterization results, which depend on the configuration of Gaussian derivative filters (GDFs), FMOSIGDFs and FMOOGDFs
Regarding the local SS expression, we divide the original image into the nonoverlapped subimages with size of 32 × 32; namely, the samples in the BSc, BLc, CSc, and CF are partitioned into 16 subimages, while the samples in the TD are divided into 300 subimages, each subimage is treated as an independent image and the expanded TP feature are extracted as described in the formula (41)

Summary

Introduction

Texture is an important visual perception cue ubiquitously existing in almost all-natural images. Developing a TPC or identification system mainly involves two steps, designing effective algorithm for TP feature extraction [2] and training a classifier for TPC [4, 5]. But cannot be defined or described effectively in the CV. The TPC is still a challenging task due to the unclear definition of the concept TP, the wide ranges of natural texture types, and the large intraclass variations in TPs [10]. To facilitate CV application or digital image processing and analysis, researchers have proposed many approximation methods to describe these perceptions depending upon their specific definition of TP

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Conclusion