Abstract
In recent years, big data technology and its corresponding research have become a research hotspot in various fields. The application of persistent homology to analyze all kinds of big data is one of the research methods that have attracted much attention. This paper mainly studies the application of continuous homology to qualitative analysis of RGB three-channel image, extracts the topological invariant features of the image, and writes them into the persistence graph. The difference and similarity of “persistence map” of different images are measured by Wasserstein distance. For similar images, there is a smaller distance, while the distance between different images is larger. This method can better distinguish images with similar topologies.
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Acknowledgments
This paper is supported by Heilongjiang Provincial Natural Science Foundation of China (LH2020F008).
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Ma, J., Zhang, L., Sun, H., Zhao, Z. (2022). Topological Feature Analysis of RGB Image Based on Persistent Homology. In: Hassanien, A.E., Xu, Y., Zhao, Z., Mohammed, S., Fan, Z. (eds) Business Intelligence and Information Technology. BIIT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-030-92632-8_39
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DOI: https://doi.org/10.1007/978-3-030-92632-8_39
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