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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Petersen Graph Multi-Orientation Based Multi-Scale Ternary Pattern (PGMO-MSTP): An Efficient Descriptor for Texture

Issam El Khadiri, Youssef El Merabet, Ahmad S Tarawneh

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|April 8, 2021

Summary

This summary is machine-generated.

A new texture classification method, Petersen Graph Multi-Orientation based Multi-Scale Ternary Pattern (PGMO-MSTP), effectively handles image variations. This novel descriptor outperforms existing methods in texture and material classification tasks.

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Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Texture classification is challenging due to variations in rotation, illumination, scale, and viewpoint.
Existing methods like Local Graph Structure (LGS) and Local Ternary Patterns (LTP) have limitations.

Purpose of the Study:

To propose a novel image feature descriptor, Petersen Graph Multi-Orientation based Multi-Scale Ternary Pattern (PGMO-MSTP), for robust texture and material classification.
To overcome the shortcomings of existing LTP-like and LGS-like descriptors.

Main Methods:

Developed single-scale Petersen Graph-based Ternary Pattern descriptors (PGTPh and PGTPv) encoding 5x5 image patches.
Utilized Petersen graph-shaped sampling structures to capture pixel relationships.
Combined histograms from PGTPh and PGTPv to create the multi-scale PGMO-MSTP model.

Main Results:

PGMO-MSTP demonstrated superior performance against state-of-the-art handcrafted and deep learning-based texture descriptors.
Experiments on sixteen challenging texture datasets confirmed the effectiveness of the proposed method.
Statistical analysis using the Wilcoxon signed rank test indicated PGMO-MSTP as the top-performing descriptor across all datasets.

Conclusions:

The proposed PGMO-MSTP descriptor offers a robust and effective solution for texture and material classification, especially under challenging image variations.
PGMO-MSTP represents a significant advancement in texture analysis, outperforming current leading methods.