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基于持续同调与复杂网络的图像形状分类算法

魏欣 彭宁宁

计算机工程2026,Vol.52Issue(9):218-227,10.
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计算机工程2026,Vol.52Issue(9):218-227,10.DOI:10.19678/j.issn.1000-3428.0070529

基于持续同调与复杂网络的图像形状分类算法

Image Shape Classification Algorithm Based on Persistent Homology and Complex Networks

魏欣 1彭宁宁1

作者信息

  • 1. 武汉理工大学数学与统计学院,湖北武汉 430070
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摘要

Abstract

To address the problems of insufficient stability in complex network construction and poor classification performance of shape features extracted in complex situations in existing complex network-based image shape classification algorithms,this paper proposes an image shape classification algorithm based on persistent homology and complex networks.The algorithm combines complex networks with Vietoris-Rips filtration to construct a persistent complex network on the image contour point cloud.Persistent homology is used to compute global topological features of different dimensions within the persistent complex network.Degree features and correlation degree features extracted from the degree distribution are selected as local shape features,and are fused with global topological features respectively to obtain two feature sets,PHCND and PHCNJD,enabling a richer shape representation of the images.The fused feature vectors are classified using Linear Discriminant Analysis(LDA).Comparative experiments are conducted with other traditional algorithms as well as a deep learning algorithm ResNet-50 on nine public image datasets.Meanwhile,ablation studies are designed to validate the effectiveness of the global topological features and the complementarity between local shape features and global topological features within the persistent complex network.Experimental results show that the proposed algorithm achieves the highest accuracy and F1-score on five public datasets,with improvements of 2.2%-30.3%in classification accuracy and 2.2%-30.9%in F1-score compared to seven traditional image shape classification algorithms.These results validate the effectiveness of the proposed algorithm for image shape classification,and demonstrate that the extracted shape features exhibit certain robustness.

关键词

图像分类/形状特征提取/复杂网络/持续同调/持续性复形

Key words

image classification/shape feature extraction/complex network/persistent homology/persistence complex

分类

信息技术与安全科学

引用本文复制引用

魏欣,彭宁宁..基于持续同调与复杂网络的图像形状分类算法[J].计算机工程,2026,52(9):218-227,10.

基金项目

国家自然科学基金(11701438). (11701438)

计算机工程

1000-3428

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