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[2602.14846] Multi-dimensional Persistent Sheaf Laplacians for Image Analysis
Summary
This paper introduces a multi-dimensional persistent sheaf Laplacian (MPSL) framework for image analysis, enhancing dimensionality reduction methods by leveraging multiple dimensions simultaneously.
Why It Matters
The proposed MPSL framework addresses the limitations of traditional dimensionality reduction techniques like PCA, which can be sensitive to the choice of reduced dimensions. By utilizing a multi-dimensional approach, this method aims to improve the stability and performance of image classification tasks, making it significant for advancements in computer vision.
Key Takeaways
- MPSL framework enhances image analysis by using multiple dimensions.
- It overcomes PCA's limitations related to dimension selection.
- The method provides stable performance across various reduced dimensions.
- Statistical summaries from multiple scales improve image representation.
- Experimental results demonstrate consistent improvements over PCA baselines.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.14846 (cs) [Submitted on 16 Feb 2026] Title:Multi-dimensional Persistent Sheaf Laplacians for Image Analysis Authors:Xiang Xiang Wang, Guo-Wei Wei View a PDF of the paper titled Multi-dimensional Persistent Sheaf Laplacians for Image Analysis, by Xiang Xiang Wang and 1 other authors View PDF HTML (experimental) Abstract:We propose a multi-dimensional persistent sheaf Laplacian (MPSL) framework on simplicial complexes for image analysis. The proposed method is motivated by the strong sensitivity of commonly used dimensionality reduction techniques, such as principal component analysis (PCA), to the choice of reduced dimension. Rather than selecting a single reduced dimension or averaging results across dimensions, we exploit complementary advantages of multiple reduced dimensions. At a given dimension, image samples are regarded as simplicial complexes, and persistent sheaf Laplacians are utilized to extract a multiscale localized topological spectral representation for individual image samples. Statistical summaries of the resulting spectra are then aggregated across scales and dimensions to form multiscale multi-dimensional image representations. We evaluate the proposed framework on the COIL20 and ETH80 image datasets using standard classification protocols. Experimental results show that the proposed method provides more stable performance across a wide range of reduced dimensions and achieves consist...