[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3837/tiis.2021.06.008

A Study on Pagoda Image Search Using Artificial Intelligence (AI) Technology for Restoration of Cultural Properties

Lee, ByongKwon (Department of Multimedia, Seowon University)
Kim, Soo Kyun (Department of Computer Engineering, Jeju National University)
Kim, Seokhun (Department of E-Commerce, Paichai University)

Publication Information

KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.6, 2021 , pp. 2086-2097 More about this Journal

Abstract

The current cultural assets are being restored depending on the opinions of experts (craftsmen). We intend to introduce digitalized artificial intelligence techniques, excluding the personal opinions of experts on reconstruction of such cultural properties. The first step toward restoring digitized cultural properties is separation. The restoration of cultural properties should be reorganized based on recorded documents, period historical backgrounds and regional characteristics. The cultural properties in the form of photographs or images should be collected by separating the background. In addition, when restoring cultural properties most of them depend a lot on the tendency of the restoring person workers. As a result, it often occurs when there is a problem in the accuracy and reliability of restoration of cultural properties. In this study, we propose a search method for learning stored digital cultural assets using AI technology. Pagoda was selected for restoration of Cultural Properties. Pagoda data collection was collected through the Internet and various historical records. The pagoda data was classified by period and region, and grouped into similar buildings. The collected data was learned by applying the well-known CNN algorithm for artificial intelligence learning. The pagoda search used Yolo Marker to mark the tower shape. The tower was used a total of about 100-10,000 pagoda data. In conclusion, it was confirmed that the probability of searching for a tower differs according to the number of pagoda pictures and the number of learning iterations. Finally, it was confirmed that the number of 500 towers and the epochs in training of 8000 times were good. If the test result exceeds 8,000 times, it becomes overfitting. All so, I found a phenomenon that the recognition rate drops when the enemy repeatedly learns more than 8,000 times. As a result of this study, it is believed that it will be helpful in data gathering to increase the accuracy of tower restoration.

Keywords

Enter key words or phrases in alphabetical order; separated by commas;

Citations & Related Records

Reference

1	W. Guo, Y. Li, W. Li and M. Sun, "Image-Based Modeling of Virtual Pagoda of China," in Proc. of 2008 International Conference on Multimedia and Ubiquitous Engineering (mue 2008), Busan, pp. 9-14, 2008.
2	W. Lan, J. Dang, Y. Wang and S. Wang, "Pedestrian Detection Based on YOLO Network Model," in Proc. of 2018 IEEE International Conference on Mechatronics and Automation (ICMA), Changchun, pp. 1547-1551, 2018.
3	Z. Xu, M. Strake and T. Fingscheidt, "Concatenated Identical DNN (CI-DNN) to Reduce Noise-Type Dependence in DNN-Based Speech Enhancement," in Proc. of 2019 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain, pp. 1-5, 2019.
4	F. K. Noble, "Comparison of OpenCV's feature detectors and feature matchers," in Proc. of 2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Nanjing, pp. 1-6, 2016.
5	Jin-Seob Kim, "A pitch-synchronous speech analysis and synthesis method for DNN-SPSS system," in Proc. of 2016 IEEE International Conference on Digital Signal Processing, pp.408-411, 2016.
6	S. Carata, R. Mihaescu, E. Barnoviciu, M. Chindea, M. Ghenescu and V. Ghenescu, "Complete Visualisation, Network Modeling and Training, Web Based Tool, for the Yolo Deep Neural Network Model in the Darknet Framework," in Proc. of 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, pp. 517-523, 2019.
7	X. Li et al., "COCO-CN for Cross-Lingual Image Tagging, Captioning, and Retrieval," IEEE Transactions on Multimedia, vol. 21, no. 9, pp. 2347-2360, Sept. 2019. DOI
8	S. Chen and W. Lin, "Embedded System Real-Time Vehicle Detection based on Improved YOLO Network," in Proc. of 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Chongqing, China, pp. 1400-1403, 2019.
9	W. Blewitt, G. Ushaw and G. Morgan, "Applicability of GPGPU Computing to Real-Time AI Solutions in Games," IEEE Transactions on Computational Intelligence and AI in Games, vol. 5, no. 3, pp. 265-275, Sept. 2013. DOI
10	H. Ketout, J. Gu and G. Horne, "MVN_CNN and UBN_CNN for endocardial edge detection," in Proc. of 2011 Seventh International Conference on Natural Computation, Shanghai, pp. 781-785, 2011.
11	WY. Nie, P. Sommella, M. O'Nils, C. Liguori and J. Lundgren, "Automatic Detection of Melanoma with Yolo Deep Convolutional Neural Networks," in Proc. of 2019 E-Health and Bioengineering Conference (EHB), Iasi, Romania, pp. 1-4, 2019.
12	S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understanding of a convolutional neural network," in Proc. of 2017 International Conference on Engineering and Technology (ICET), Antalya, pp. 1-6, 2017.
13	Y. Lu, L. Zhang and W. Xie, "YOLO-compact: An Efficient YOLO Network for Single Category Real-time Object Detection," in Proc. of 2020 Chinese Control And Decision Conference (CCDC), Hefei, China, pp. 1931-1936, 2020.
14	A. M. Garcia, R. S. Requena, A. Alberich-Bayarri, G. Garcia-Marti, M. Egea and C. M. Martinez, "Coco-Cloud project: Confidential and compliant clouds," in Proc. of IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Valencia, pp. 227-230, 2014.