The Ancient Ceramics Identification Methods Based on Non-Linear Support Vector Machines

Abstract:

With the ceramics market's developing, the use of image processing and intelligent algorithm is applied to the ancient ceramics recognition and appreciation is one of the most challenging issues in the field of ancient ceramics. Article focuses on selected Ming Qing Dynasty blue and white porcelain as research samples, and explore how to extract the effective image recognition features of ancient ceramics, and to quantify the comparison, given the ancient craft of evaluation index system, and improve the identification of categories of, and appreciation evaluation model to extract special recognition feature, image preprocessing, discussion handwritten the key technology of Chinese character segmentation, feature extraction and classifier design a variety of methods, and non-linear support vector machine analysis method using multiple classifiers based, so that the sample's accuracy greatly improved.

• Periodical: Applied Mechanics and Materials (Volumes 278-280)
• Pages: 1201-1204
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.278-280.1201
• Online since: January 2013
• Authors: Ru Zhang
• Keywords: Identification, Ming and Qing Dynasty Blue and White Porcelain, Multi-Classifier, Non-Linear Support Vector Machine

References

[1] Shi Fanhui, Tong Xuefeng．Application Research of SVM for Small-Set Of -Line Handwriten Chinese Characters Recognition: Computer Engineering, Vol. 28(2002), p.154.

[2] Hidetoshi Miyao, Minoru Maruyama．Virtual Example Synthesis Based on PCA for Of—Line Handwritten Character Recognition: Computer Science, Vol. 72 (2006) , p.96.

DOI: 10.1007/11669487_9

[3] Liu Dong, Huang Xiangnian．Recognition of Of-Line Handwritten Chinese Character by Using Decision Tree Based on Hierarchy Decomposition: International Conference on Advanced Computer Control, Vol. 81(2009), p.351.

DOI: 10.1109/icacc.2009.21

[4] Zhu Minjue, Zhu Ningbo, Yuan Yi．A Handwritten Chinese Character Recognition Method Based on Multi-Classifier Ensemble: Computer Engineering＆Science, Vol. 31 (2009) , p.36.

[5] Nie Xuan, Zhao Rongchun, Zhang Yanning, Jiang Zetao, Zhang Xiaoyan. On Achieving Better Fault-Tolerant Capability for Recognizing Heavily Stained Primed Chinese Characters with a Four-Layer Fuzzy Neural Network: Journal of Northwestern Poly-technical University, Vol. 22 (2004).