The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++
Abstract This study aims to address the issues of accuracy and efficiency in sculpture image classification. Due to the diversity and complexity of sculpture images, traditional image processing algorithms perform poorly in capturing the sculptures’ intricate shapes and structural features, resultin...
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Nature Portfolio
2025-05-01
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| Online Access: | https://doi.org/10.1038/s41598-025-01949-5 |
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| author | Xuhui Wang |
| author_facet | Xuhui Wang |
| author_sort | Xuhui Wang |
| collection | DOAJ |
| description | Abstract This study aims to address the issues of accuracy and efficiency in sculpture image classification. Due to the diversity and complexity of sculpture images, traditional image processing algorithms perform poorly in capturing the sculptures’ intricate shapes and structural features, resulting in suboptimal classification and recognition performance. To overcome this challenge, this study proposes an innovative image classification method that combines the ResNet50 model from the Deep Convolutional Neural Network (DCNN) with the K-means++ clustering algorithm. ResNet50 is chosen for its powerful feature extraction capabilities and outstanding performance in image classification tasks. At the same time, K-means++ is selected for its optimized initial centroid selection strategy, which enhances the stability and reliability of clustering. After the final convolutional layer of ResNet50, a self-attention module is added. This module learns and generates an attention map, which guides the model on which areas of the image to focus on in subsequent processing. ResNet50 includes residual blocks, each containing multiple convolutional layers and a skip connection, enabling the network to learn differences between inputs and outputs rather than directly learning outputs, thus improving performance. Initially, ResNet50 extracts feature vectors from original images, which are inputted into the K-means + + algorithm for clustering. K-means + + automatically partitions these feature vectors into different categories, achieving unsupervised image classification. The CMU-MINE architectural sculpture dataset is utilized in the experimental section, with ViT-Base, EfficientNet-B4, and ConvNeXt-Tiny as benchmarks to evaluate the proposed ResNet50 + K-means + + image classification approach. The final model achieves a loss value of 0.155 and a recall of 98.9%, significantly outperforming the other three models. In conclusion, performing feature point matching during three-dimensional reconstruction is crucial. This study employs a combined image classification method using the ResNet50 and K-means + + algorithm, optimizing the accuracy issues of traditional classification methods and achieving promising classification results. |
| format | Article |
| id | doaj-art-e0bb9db7d47140d7b280955a6b2ffd8e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-e0bb9db7d47140d7b280955a6b2ffd8e2025-08-20T03:08:40ZengNature PortfolioScientific Reports2045-23222025-05-0115111410.1038/s41598-025-01949-5The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++Xuhui Wang0College of Fine Arts, Sichuan University of Science & EngineeringAbstract This study aims to address the issues of accuracy and efficiency in sculpture image classification. Due to the diversity and complexity of sculpture images, traditional image processing algorithms perform poorly in capturing the sculptures’ intricate shapes and structural features, resulting in suboptimal classification and recognition performance. To overcome this challenge, this study proposes an innovative image classification method that combines the ResNet50 model from the Deep Convolutional Neural Network (DCNN) with the K-means++ clustering algorithm. ResNet50 is chosen for its powerful feature extraction capabilities and outstanding performance in image classification tasks. At the same time, K-means++ is selected for its optimized initial centroid selection strategy, which enhances the stability and reliability of clustering. After the final convolutional layer of ResNet50, a self-attention module is added. This module learns and generates an attention map, which guides the model on which areas of the image to focus on in subsequent processing. ResNet50 includes residual blocks, each containing multiple convolutional layers and a skip connection, enabling the network to learn differences between inputs and outputs rather than directly learning outputs, thus improving performance. Initially, ResNet50 extracts feature vectors from original images, which are inputted into the K-means + + algorithm for clustering. K-means + + automatically partitions these feature vectors into different categories, achieving unsupervised image classification. The CMU-MINE architectural sculpture dataset is utilized in the experimental section, with ViT-Base, EfficientNet-B4, and ConvNeXt-Tiny as benchmarks to evaluate the proposed ResNet50 + K-means + + image classification approach. The final model achieves a loss value of 0.155 and a recall of 98.9%, significantly outperforming the other three models. In conclusion, performing feature point matching during three-dimensional reconstruction is crucial. This study employs a combined image classification method using the ResNet50 and K-means + + algorithm, optimizing the accuracy issues of traditional classification methods and achieving promising classification results.https://doi.org/10.1038/s41598-025-01949-5Sculpture creationDeep convolutional neural networkClustering algorithmsImage classification |
| spellingShingle | Xuhui Wang The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++ Scientific Reports Sculpture creation Deep convolutional neural network Clustering algorithms Image classification |
| title | The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++ |
| title_full | The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++ |
| title_fullStr | The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++ |
| title_full_unstemmed | The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++ |
| title_short | The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++ |
| title_sort | analysis of sculpture image classification in utilization of 3d reconstruction under k means |
| topic | Sculpture creation Deep convolutional neural network Clustering algorithms Image classification |
| url | https://doi.org/10.1038/s41598-025-01949-5 |
| work_keys_str_mv | AT xuhuiwang theanalysisofsculptureimageclassificationinutilizationof3dreconstructionunderkmeans AT xuhuiwang analysisofsculptureimageclassificationinutilizationof3dreconstructionunderkmeans |