School of Urban Construction, Wuhan University of Science and Technology, Wuhan, Hubei 430070, China.
Wuhan Business University, Wuhan, Hubei 430000, China.
Comput Intell Neurosci. 2022 Aug 21;2022:4674620. doi: 10.1155/2022/4674620. eCollection 2022.
In this paper, a CNN model for color element data analysis of the urban spatial environment is constructed through an in-depth study of color element data analysis. This paper investigates a high-order structure formed by a few nodes; it proposes a motif-based graph autoencoder MODEL, combining redefined first- and second-order similarities and perfectly integrating motif structure and autoencoder. The algorithm first proposes an efficient graph transformation method to add the influence of central nodes. It then offers a primary awareness mechanism to aggregate the information of noncentral neighbors. Cen GCN_D and Cen GCN_E outperform the latest algorithms in node classification, link prediction, node clustering, and network visualization. As the number of network layers increases, the advantages of these two variants become progressively more prominent. This paper uses a support vector machine to implement classification validation based on CNN. The experimental results show that when 450 images are randomly selected as training data, the classification accuracy obtained by using the features of different CNN output layers is distributed between 91.4% and 95.2%. When the training set of the experiment reaches more than 300, the accuracy can exceed 90%, and the experimental results corresponding to different training sets a more stable trend. Finally, the trained classifier model is obtained in this thesis, which achieves the purpose of fast classification prediction based on CNN for color element data analysis of urban spatial environments.
本文通过深入研究色彩元素数据分析，构建了一种用于城市空间环境色彩元素数据分析的 CNN 模型。本文研究了由少数节点形成的高阶结构；提出了基于模式的图自动编码器模型，结合了重新定义的一阶和二阶相似度，并完美地集成了模式结构和自动编码器。该算法首先提出了一种有效的图变换方法来添加中心节点的影响。然后，它提供了一种主要的感知机制来聚合非中心邻居的信息。在节点分类、链路预测、节点聚类和网络可视化方面，Cen GCN_D 和 Cen GCN_E 优于最新的算法。随着网络层数的增加，这两种变体的优势变得越来越明显。本文使用支持向量机基于 CNN 实现分类验证。实验结果表明，当随机选择 450 张图像作为训练数据时，使用不同 CNN 输出层的特征获得的分类精度分布在 91.4%到 95.2%之间。当实验的训练集达到 300 多个时，准确率可以超过 90%，并且不同训练集对应的实验结果呈现出更稳定的趋势。最后，本文获得了经过训练的分类器模型，实现了基于 CNN 对城市空间环境色彩元素数据进行快速分类预测的目的。
