基于持续同调与复杂网络的图像形状分类算法OA

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.