In this paper, the Isometric Mapping (ISOMAP) algorithm is applied to recognize oracle bone inscription images. First, the sample set undergoes denoising and size normalization as preprocessing steps. Subsequently, a gray-value matrix is extracted from the images as their feature representation. The ISOMAP algorithm is then implemented to obtain a low-dimensional embedding of the sample set. Following this, the classification is performed by selecting the label corresponding to the nearest neighbor category with the highest frequency around the test sample. By optimizing the parameters of the
-neighborhood and
, the recognition accuracy reaches 93.3%. Finally, the performance of ISOMAP is compared with other manifold learning algorithms. Experimental results demonstrate that ISOMAP achieves a higher average recognition rate and lower computational time compared to its counterparts. Therefore, ISOMAP algorithm proves to be an effective tool for oracle bone inscription recognition.
References
[1]
Fu, X., Yang, Z., Zeng, Z., Zhang, Y. and Zhou, Q. (2022) Improvement of Oracle Bone Inscription Recognition Accuracy: A Deep Learning Perspective. ISPRS International Journal of Geo-Information, 11, Article 45. https://doi.org/10.3390/ijgi11010045
[2]
Keightley, D.N. (1979) The Shang State as Seen in the Oracle-Bone Inscriptions. Early China, 5, 25-34. https://doi.org/10.1017/s0362502800006118
[3]
Huang, S., Wang, H., Liu, Y., Shi, X. and Jin, L. (2019) OBC306: A Large-Scale Oracle Bone Character Recognition Dataset. 2019 International Conference on Document Analysis and Recognition (ICDAR), Sydney, 20-25 September 2019, 681-688. https://doi.org/10.1109/icdar.2019.00114
[4]
Cheung, C. (2018) The Chinese History That Is Written in Bone. American Scientist, 106, 133-134. https://doi.org/10.1511/2018.106.3.133
[5]
Song, Z. (2016) Collection of Oracle Bone Rubbings of Hu. Shanghai Ancient Books Publishing House.
[6]
Wang, L., Wang, C.J. and Jiao, Q.J. (2023) Research on Handwriting Oracle Recognition Based on EasyDL. Electronic Technology and Software Engineering, 3, 184-187.
[7]
Feng, Y. (1996) Recognition of Jia Gu Wen Based on Graph Theory. Journal of Electronics and Information Technology, 18, 41-47.
[8]
Lu, X., Li, M., Cai, K., et al. (2010) A Graphic-Based Method for Chinese Oracle-Bone Classification. Journal of Beijing Information Science or Technology University, 25, 92-96.
[9]
Chua, L.O. (1997) CNN: A Vision of Complexity. International Journal of Bifurcation and Chaos, 7, 2219-2425. https://doi.org/10.1142/s0218127497001618
[10]
Liu, G. (2018) Oracle-Bone Inscription Recognition Based on Deep Convolutional Neural Network. Journal of Computers, 8, 1442-1450. https://doi.org/10.17706/jcp.13.12.1442-1450
[11]
Meng, L., Kamitoku, N. and Yamazaki, K. (2018) Recognition of Oracle Bone Inscriptions Using Deep Learning Based on Data Augmentation. 2018 Metrology for Archaeology and Cultural Heritage (MetroArchaeo), Cassino, 22-24 October 2018, 33-38. https://doi.org/10.1109/metroarchaeo43810.2018.9089769
[12]
Chen, T., Qian, Y., Pei, J., Wu, S., Wu, J., Li, L., et al. (2020) A Study on Encoding-Based Oracle Bone Script Recognition. Journal of Chinese Writing Systems, 4, 281-290. https://doi.org/10.1177/2513850220952890
[13]
Liu, Y.G. and Liu, G.Y. (2017) Oracle Bone Inscription Recognition Based on SVM. Journal of Anyang Normal University, 2, 54-56.
[14]
Tenenbaum, J.B., Silva, V.D. and Langford, J.C. (2000) A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 290, 2319-2323. https://doi.org/10.1126/science.290.5500.2319
[15]
Xu, H. (2017) The Methods of Information Geometry in Wireless Sensor Networks. Ph.D. Thesis, Beijing Institute of Technology.
[16]
Zhao, X. and Zhang, S. (2011) Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap. Sensors, 11, 9573-9588. https://doi.org/10.3390/s111009573
[17]
Liu, J., Wang, H., Zhou, X. and Luo, F. (2013) Face Recognition Based on Improved Isometric Feature Mapping Algorithm. Journal of Computer Applications, 33, 76-79. https://doi.org/10.3724/sp.j.1087.2013.00076
[18]
Weng, X. and Qin, S. (2012) Classification of Multivariate Time Series Using Supervised Isomap. 2012 Third Global Congress on Intelligent Systems, Wuhan, 6-8 November 2012, 136-139. https://doi.org/10.1109/gcis.2012.31
[19]
Yang, H. and Li, B. (2019) Node Localization of Wireless Sensor Network Based on the Kernel Matrix ISOMAP Algorithm. Journal of East China Normal University, 9, 115-123.
[20]
Wang, J. (2012) Geometric Structure of High-Dimensional Data and Dimensionality Reduction. Higher Education Press.
