Skip to main content
SpringerLink
Log in

Artistic features extraction from chinese calligraphy works via regional guided filter with reference image

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Chinese calligraphy is a unique visual art, and and is one of the material basis of China’s traditional cultural heritage. However, time had caused the old calligraphy works to weathering and damages, so it is necessary to utilize advanced technologies to protect those works. One of those technologies is digital imaging, and the obtained images by digital imaging can preserve the visual information of calligraphy works better, furthermore, they can be used in further researches. While the basic works for those researches are to extract the artistic features which include two elements, i.e., form and spirit. However, most of the existing methods only extract the form and ignore the characters’ spirit, especially they are insensitive to the slight variation in complex ink strokes. To solve these problems, this paper proposes an extraction method based on regional guided flter (RGF) with reference images, which is generated by KNN matting and used as the input image for RGF. Since RGF is sensitive to the slight variation of ink, so the detailed information of the inside of strokes can be detected better. Besides, unlike the past works, which filter the whole strokes, RGF filters the inside of strokes and edges in different windows respectively, which results in that the edges are preserved accurately. Results from a deployment of several famous Chinese calligraphy works demonstrate that our method can extract more accurate and complete form and spirit with lower error rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Cardoso A, Wichert A (2013) Handwritten digit recognition using biologically inspired features. Neurocomputing 99(1):575–580

    Article  Google Scholar 

  2. Chang X, Ma Z, Yang Y, Zeng Z, Hauptmann AG (2016) Bi-level semantic representation analysis for multimedia event detection. IEEE Trans Cybern, 1–18

  3. Chang X, Nie F, Wang S, Yang Y, Zhou X, Zhang C (2016) Compound rank-k projections for bilinear analysis. IEEE Trans Neural Netw Learn Syst 27(7):1502–1513

  4. Chang X, Nie F, Yang Y, Zhang C, Huang H (2016) Convex sparse pca for unsupervised feature learning. ACM Trans Knowl Discov Data 11(1):1–16

    Article  Google Scholar 

  5. Chang X, Yang Y (2014) Semi-supervised feature analysis by mining correlations among multiple tasks. IEEE Trans Neural Netw, 1–12

  6. Chang X, Yang Y, Hauptmann AG, Xing EP, Yu YL (2015) Semantic concept discovery for large-scale zero-shot event detection. In: International conference on artificial intelligence, pp 2234–2240

  7. Chang X, Yu YL, Yang Y, Xing EP (2016) Semantic pooling for complex event analysis in untrimmed videos. IEEE Trans Pattern Anal Mach Intell PP (99):1–1

  8. Chen J, Zhu F (2009) Hierarchical matching for chinese calligraphic retrieval using skeleton similarity. Int J Inf Technol Comput Sci (IJITCS) 1(1):41

    Article  Google Scholar 

  9. Chen Q, Li D, Tang C-K (2013) Knn matting. IEEE Trans Pattern Anal Mach Intell 35(9):2175–2188

    Article  Google Scholar 

  10. Draper NR, Smith H, Pownell E (1966) Applied regression analysis, vol 3. Wiley, New York

    Google Scholar 

  11. He K, Sun J, Tang X (2010) Guided image filtering, in: Computer vision–ECCV 2010. Springer, pp 1–14

  12. Huang ZK, Li ZH, Huang H, Li ZB, Hou LY Comparison of different image denoising algorithms for chinese calligraphy images. Neurocomputing

  13. Lee P, Wu Y (2011) Nonlocal matting. In: Proceedings of the 2012 IEEE conference on computer vision and pattern recognition (CVPR), IEEE Computer Society, pp 2193–2200

  14. Li D (2012) Knn matting. In: Proceedings of the 2012 IEEE conference on computer vision and pattern recognition (CVPR), IEEE Computer Society, pp 869-876

  15. Li W, Song Y, Zhou C (2014) Computationally evaluating and synthesizing chinese calligraphy. Neurocomputing 135(13):299–305

    Article  Google Scholar 

  16. Lu W-M, Wu J-Q, Wei B-G, Zhuang Y-T (2011) Efficient shape matching for chinese calligraphic character retrieval. J Zhejiang Univ Sci C 12(11):873–884

    Article  Google Scholar 

  17. Mao T, Wu J, Lin PGYXY (2013) Calligraphy word style recognition by knn based feature library filtering. In: Proceedings of the 3rd international conference on multimedia technology, pp 934– 941

  18. Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP 2(1):331–340

    Google Scholar 

  19. Romero R, Berger R, Thibadeau R, Touretzky D (1999) Neural network classifiers for optical chinese character recognition. Ceskoslovensk Pediatrie 47(5):257

    Google Scholar 

  20. Shi C, Xiao J, Jia W, Xu C (2013) Character feature integration of chinese calligraphy and font. In: IS&t/SPIE electronic imaging, international society for optics and photonics, pp 86580m– 86580m

  21. Tan J, Lai JH,Wang CD,WangWX, Zuo XX (2012) A new handwritten character segmentation method based on nonlinear clustering. Neurocomputing, 89, 213-219

    Article  Google Scholar 

  22. Wen J, Fang B, Chen J, Tang YY, Chen HX (2012) Fragmented edge structure coding for chinese writer identification. Neurocomputing 86(4):45–51

    Article  Google Scholar 

  23. Wong ST, Leung H, Ip H-S (2006) Fitting ellipses to a region with application in calligraphic stroke reconstruction. In: 2006 IEEE international conference on image processing. IEEE, pp 397–400

  24. Yasnoff WA, Mui JK, Bacus JW (1977) Error measures for scene segmentation. Pattern Recogn 9(4):217–231

    Article  Google Scholar 

  25. Zhang Y Deep convolutional network for handwritten chinese character recognition. Computer Science Department, Stanford University

  26. Zhang X, Nagy G (2011) The cadal calligraphic database. In: Proceedings of the 2011 workshop on historical document imaging and processing. ACM, pp 37–42

  27. Zhang Y-F, Zhou M-Q, Geng G-H, Feng J (2010) Face appearance reconstruction based on a regional statistical craniofacial model (rcsm). In: 2010 20th international conference on pattern recognition (ICPR). IEEE, pp 1670–1673

  28. Zheng X, Miao Q, Shi Z, Fan Y, ShuiW(2015) A new artistic information extraction method with multi channels and guided filters for calligraphy works. Multimed Tools Appl 1–26

  29. Zhu Y, Lucey S (2015) Convolutional sparse coding for trajectory reconstruction. IEEE Trans Pattern Anal Mach Intell 37(3):529–540

    Article  Google Scholar 

  30. Zhuang Y, Lu W, Wu J (2009) Latent style model: discovering writing styles for calligraphy works. J Vis Commun Image Represent 20(2):84–96

    Article  Google Scholar 

  31. Zhuang Y, Yuan C (2010) A hybrid-feature-based efficient retrieval over chinese calligraphic manuscript image repository. In: Web-age information management. Springer, pp 544–550

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China (Grant No. 61202198), and Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2016 JQ6068). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, China

    Lei Wang, Xiaoqing Gong, Yongqin Zhang, Pengfei Xu, Xiaojiang Chen, Dingyi Fang & Jun Guo

  2. Department of Culture Heritage and Museology, Zhejiang University, Hangzhou, China

    Xia Zheng

Authors
  1. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoqing Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongqin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pengfei Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaojiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dingyi Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xia Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xia Zheng or Jun Guo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, L., Gong, X., Zhang, Y. et al. Artistic features extraction from chinese calligraphy works via regional guided filter with reference image. Multimed Tools Appl 77, 2973–2990 (2018). https://doi.org/10.1007/s11042-017-4688-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4688-8

Keywords

Search

Navigation