用沃罗诺伊图+图卷积提升图像分类精度与效率
Fast Graph Neural Network for Image Classification
- 将图像转为基于沃罗诺伊图的图结构，用图卷积网络处理像素关系
- 在多个基准数据集上准确率超越现有方法，尤其擅长细粒度和复杂场景
- 适合关注图神经网络在视觉任务中应用的研究者与工程师
图像分类的快速发展很大程度上得益于图卷积网络（GCNs）的应用，其为处理复杂数据结构提供了稳健框架。本文提出一种新方法，将GCNs与沃罗诺伊图结合，通过有效建模像素间关系来提升图像分类性能。与传统卷积神经网络不同，该方法将图像表示为图，其中像素或区域作为节点，再利用对应的德劳内三角剖分优化图结构。所提模型在多个基准数据集上显著提升了预处理效率与分类准确率，优于当前最先进方法，尤其在复杂场景和细粒度类别识别任务中表现突出。交叉验证实验结果证实了将GCNs与沃罗诺伊图结合在图像分类中的有效性。本研究不仅为图像分类提供了新视角，也拓展了基于图的学习范式在计算机视觉和非结构化数据分析中的应用潜力。
原文摘要 · Abstract (English)
The rapid progress in image classification has been largely driven by the adoption of Graph Convolutional Networks (GCNs), which offer a robust framework for handling complex data structures. This study introduces a novel approach that integrates GCNs with Voronoi diagrams to enhance image classification by leveraging their ability to effectively model relational data. Unlike conventional convolutional neural networks (CNNs), our method represents images as graphs, where pixels or regions function as vertices. These graphs are then refined using corresponding Delaunay triangulations, optimizing their representation. The proposed model achieves significant improvements in both preprocessing efficiency and classification accuracy across various benchmark datasets, surpassing state-of-the-art approaches, particularly in challenging scenarios involving intricate scenes and fine-grained categories. Experimental results, validated through cross-validation, underscore the effectiveness of combining GCNs with Voronoi diagrams for advancing image classification. This research not only presents a novel perspective on image classification but also expands the potential applications of graph-based learning paradigms in computer vision and unstructured data analysis.
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