Paper
9 January 2025 Texture image classification based on persistent homology
Haoming Zhou
Author Affiliations +
Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134860N (2025) https://doi.org/10.1117/12.3055922
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China
Abstract
In the field of computer vision and image processing, texture provides critical visual clues about the composition of internal regions of an image. This paper proposes a novel texture image classification method using persistent homology (PH) theory from topological data analysis (TDA) to extract topological information of texture images at different scales and dimensions. We convert texture images into grayscale images and use their pixel intensity values as filtration thresholds, tracking changes in 0-dimensional and 1-dimensional persistent homology classes under different thresholds to generate persistence diagrams of the texture images. The persistence images are then converted into feature vectors. Our method is validated with 10-fold cross-validation using a random forest model on the Kylberg dataset and KTH-TIPS dataset, achieving accuracies of 99.84% and 87.90%, respectively.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Haoming Zhou "Texture image classification based on persistent homology", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134860N (9 January 2025); https://doi.org/10.1117/12.3055922
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KEYWORDS
Image classification

Image analysis

Image processing

Visualization

Data modeling

Computer vision technology

Cross validation

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