Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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An Efficient Comparing and Updating Method of Rights Management Information for Integrated Public Domain Image Search Engine

  • Kim, Il-Hwan (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Hong, Deok-Gi (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Kim, Jae-Keun (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Kim, Young-Mo (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Kim, Seok-Yoon (Dept. of Computer Science and Engineering, Soongsil University)
  • Received : 2018.12.03
  • Accepted : 2019.01.01
  • Published : 2019.01.31
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Abstract

In this paper, we propose a Rights Management Information(RMI) expression systems for individual sites are integrated and the performance evaluation is performed to find out an efficient comparing and updating method of RMI through various image feature point search techniques. In addition, we proposed a weighted scoring model for both public domain sites and posts in order to use the most latest RMI based on reliable data. To solve problem that most public domain sites are exposed to copyright infringement by providing inconsistent RMI(Rights Management Information) expression system and non-up-to-date RMI information. The weighted scoring model proposed in this paper makes it possible to use the latest RMI for duplicated images that have been verified through the performance evaluation experiments of SIFT and CNN techniques and to improve the accuracy when applied to search engines. In addition, there is an advantage in providing users with accurate original public domain images and their RMI from the search engine even when some modified public domain images are searched by users.

Keywords

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Fig. 1. Gaussian Pyramid

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Fig. 2. Orientation Histogram

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Fig. 3. Network with CNN

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Fig. 4. Network Consisting of A complete Connection Layer(Affine layer)

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Fig. 5. Example of Convolution Operation

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Fig. 6. Public Domain Image Search Engine Architecture

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Fig. 7. Average Hash

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Fig. 8. Average Hash Execution Result

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Fig. 9. Similar Image Search Results using Average Hash

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Fig. 10. dHash

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Fig. 11. SIFT Algorithm

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Fig. 12. SIFT Algorithm Result

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Fig. 13. CNN Model Construction

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Fig. 14. CNN Learning

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Fig. 15. Duplicated Images of Sites

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Fig. 16. Weighted Scoring Model-based update results

Table 1. RMI Database Scheme

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Table 2. Integrated RMI Schema of ‘Gong-U Madang’

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Table 3. Integrated RMI Schema of Flikr

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Table 4. Results of Image Feature Point Comparison Search Performance

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Table 5. Evaluation Items of Weighted Scoring Model

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Table 6. Rate this Item by Weight of the Weighted Scoring Model

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Table 7. Results of Evaluation by Public Domain Site

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Table 8. Results of Reliability Calculation by Site

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Table 9. Results of Evaluation by Duplicate Image Article

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Table 10. Results of Duplicate Image Reliability Calculation

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Table 11. Site A, D Reliability Comparison

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References

  1. H. Deok-Gi, K. Il-Hwan, K. Youngmo and K. Seok-Yoon "A Rights Management Information Updating Technique Using Image Feature Points," Proceedings of the Korean Society of Computer Information Conference. Vol. 26, No. 2, pp. 462-464, July 2018.
  2. HackerFactor , http://www.hackerfactor.com/
  3. K. hikodukue, "Python NI YORU SCRAPING & KIKAIGA KUSHU KAIHATSU TECHNIQUE" Japan-Press, pp. 301-320, 2016.
  4. D.G Lowe, "Object recognition from local scale-invariant features,"Proceedings of the Seventh IEEE International Conference on Computer Vision. Vol. 2, p 1150, September 1999.
  5. bskvision, http://http://bskyvision.com/21
  6. A. Krizhevsky, I. Sutskever and G, E, Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Proceedings of the NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, Vol 1, pp. 1097-1105, 2010.
  7. S. goki, "Deep Learning from Scratch" Japan-Press, pp.227-235, 2016.
  8. J. SangYup, K. HoYeon and C. Jae-Soo, "Estimation of Non-trained Category in Image Classification Model based on Deep-Learning," Journal of Instritute of Control, Robotics and Systems, Vol. 24, No. 9, pp. 793-801, 2018. https://doi.org/10.5302/J.ICROS.2018.18.0104
  9. K. MyungJoon and P. Byeonghwa, "A Study on Big Data Application using Scoring Model," Jounal of Actuarial Science, Vol. 7, No. 2, pp. 3-22, 2015
  10. S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 6, pp. 1137-1149, 2017.
  11. EUN-JI SEO, Il-Hwan Kim, Deok-Gi Hong, Youngmo Kim and Seok-Yoon Kim, "Integrated Multilayer Metadata Based on Intellectual Information Technology for Customization Services of Public Domain Images with Different Usage Permission of License," 28th Journal of Theoretical and Applied Information Technology, Vol. 96, No. 4, pp. 1048-1058, 2018.
  12. N. Hyun Woo, "Research on the Improvement Plan for the Protection and Use Activation of Image Copyright," KOREA SCIENCE & ART FORUM, Vol. 15, pp. 233-246, 2014 https://doi.org/10.17548/ksaf.2014.03.15.233