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Research on the Identification of Hand-Painted and Machine-Printed Thangka Using CBIR

Chunhua Pan1,*, Yi Cao2, Jinglong Ren3

1 key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission, Qinghai Minzu University, Xining, 810007, China
2Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, N9B 3P4, Canada
3 Qinghai Qianxun Information Technology Co. LTD, Xining, Qinghai, 810007, China

* Corresponding Author: Chunhua Pan. Email: email

Intelligent Automation & Soft Computing 2022, 34(2), 1081-1091. https://doi.org/10.32604/iasc.2022.028763

Abstract

Thangka is a unique painting art form in Tibetan culture. As Thangka was awarded as the first batch of national intangible cultural heritage, it has been brought into focus. Unfortunately, illegal merchants sell fake Thangkas at high prices for profit. Therefore, identifying hand-painted Thangkas from machine-printed fake Thangkas is important for protecting national intangible cultural heritage. The paper uses Content-Based Image Retrieval (CBIR) techniques to analyze the color, shape, texture, and other characteristics of hand-painted and machine- printed Thangka images, in order to identify Thangkas. Based on the database collected and established by this project team, we use Local Binary Pattern (LBP) texture analysis combined with the color histogram of Hue,aturation, Value (HSV) space, scale invariance, K-Means clustering, perceptual Difference Hash (DHASH) and other algorithms to extract the color lines and texture features of Thangka images, in order to identify hand-painted and machine-printed Thangkas. Three algorithms, LBP algorithm and HSV algorithm and DHASH algorithm, are compared, and the experimental results show that the color histogram algorithm based on HSV space is efficient. This algorithm can be applied broadly to retrieve and identify hand-painted Thangkas and help protect this precious intangible cultural heritage.

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Cite This Article

C. Pan, Y. Cao and J. Ren, "Research on the identification of hand-painted and machine-printed thangka using cbir," Intelligent Automation & Soft Computing, vol. 34, no.2, pp. 1081–1091, 2022.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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