Prof. Xia Ji Profile

Prof. Xia Ji

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Proceedings Article | 5 July 2024 Paper

Image denoising network based on involution kernel

Xia Ji, Zi Shuai Hu

Proceedings Volume 13184, 131844W (2024) https://doi.org/10.1117/12.3033117

KEYWORDS: Denoising, Image denoising, Convolution, Education and training, Network architectures, Performance modeling, Neural networks, Image segmentation, Feature extraction, Design

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This article presents CIUNet, an innovative deep neural network designed for image denoising, with the objective of efficiently eliminating noise and enhancing denoising task performance. By leveraging cutting-edge convolutiveinvolution modules, CIUNet adeptly captures non-local image information without the need for additional parameters, and is finely tuned based on the UNet architecture, facilitating a synergistic improvement of both local and non-local features. The network is engineered for end-to-end learning, ensuring minimal temporal and spatial complexity. Extensive testing on both synthetic and real datasets has underscored CIUNet's exceptional efficiency and effectiveness in image denoising, particularly notable in its handling of low-noise images. Additionally, the paper includes analyses of model complexity and ablation studies, establishing CIUNet's ability to deliver high denoising performance with reduced complexity, affirming its viability as a lightweight network solution. In sum, CIUNet's introduction marks a significant advancement in the image denoising domain, offering a novel approach and insights, alongside substantial practical application potential due to its innovative network design and denoising proficiency

Proceedings Article | 5 July 2024 Paper

Clustering ensemble based on multiscale cluster reliability

Xia Ji, Long Chen

Proceedings Volume 13184, 1318420 (2024) https://doi.org/10.1117/12.3032962

KEYWORDS: Reliability, Matrices, Cooccurrence matrices, Probability theory, Data modeling

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Clustering ensemble can effectively improve the accuracy and robustness of clustering. The quality and diversity of base clustering are crucial to the clustering ensemble effect. However, traditional clustering ensemble algorithms usually treat a base clustering result as a whole, ignoring the quality differences within the same base clustering result. Clusters of different quality have completely different contributions to clustering ensemble results, and ignoring the quality differences of clusters can seriously affect the effectiveness of clustering ensemble. To solve this problem, this paper proposes a clustering ensemble method based on multiscale cluster reliability. In this method, multiscale global structural information is first mined by performing random walks on the cluster similarity graph to obtain multiscale correlation between clusters. Then, the multiscale cluster reliability is measured by using the multiscale correlation of clusters, combined with the information entropy, and the clusters are weighted accordingly. Lastly, the clustering results are obtained by graph segmentation. Experimental results on numerous real datasets show that compared with 10 typical and state-of-the-art clustering ensemble methods, the clustering ensemble method based on multiscale cluster reliability not only performs better in clustering effect but also has higher stability.

Proceedings Article | 26 May 2023 Paper

Clustering ensemble based on sample’s representation

Xia Ji, Yan-Bing Zhang, Sheng Yao, Peng Zhao

Proceedings Volume 12700, 1270027 (2023) https://doi.org/10.1117/12.2682412

KEYWORDS: Matrices, Statistical analysis, Visualization, Transform theory

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The clustering ensemble is formed by combining clustering analysis and ensemble learning. However, most clustering ensemble methods treat all samples equally, which negatively affects the final clustering result. To this end, we propose sample’s representation to evaluate the sample’s importance in clustering comprehensively. The sample’s representation measures the clustering importance of samples from two perspectives: the stability of the relationship between the sample and its neighbor samples and the closeness of the relationship between the sample and its neighbor samples. According to the representation of each sample, we divide a dataset into cluster core and cluster halo. Then we obtain the credible underlying structure through the cluster core samples. Finally, the cluster halo samples are gradually allocated to the above structure to get the final clustering result. The working steps of the algorithm are shown on two synthetic datasets, and experiments on nine real datasets fully demonstrate that the algorithm outperforms 11 other state-of-the-art clustering ensemble methods.

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