Oil Portrait Snapshot Classification on Mobile

Yan Sun, Xiaomu Niu

2017

Abstract

In recent years, several art museums have developed smartphone applications as the e-guide in museums. However few of them provide the function of instant retrieval and identification for a painting snapshot taken by mobile. Therefore in this work we design and implement an oil portrait classification application on smartphone. The accuracy of recognition suffers greatly by aberration, blur, geometric deformation and shrinking due to the unprofessional quality of snapshots. Low-megapixel phone camera is another factor downgrading the classification performance. Carefully studying the nature of such photos, we adopts the SIPH algorithm (Scale-invariant feature transform based Image Perceptual Hashing)) to extract image features and generate image information digests. Instead of popular conventional Hamming method, we applied an effective method to calculate the perceptual distance. Testing results show that the proposed method conducts satisfying performance on robustness and discriminability in portrait snapshot identification and feature indexing.

References

  1. Bartolini, F. et al., 2003. Applications of image processing technologies to fine arts. In Optical Metrology (pp. 12- 23). International Society for Optics and Photonics.
  2. Johnson, C. et al., 2008. Image processing for artist identification. IEEE Signal Processing Magazine, 25(4), pp.37-48.
  3. Nack, F., et al., 2001. The role of high-level and low-level features in style-based retrieval and generation of multimedia presentations. New Review of Hypermedia and Multimedia, 7(1), pp.39-65.
  4. Stork, D. G., 2009. Computer vision and computer graphics analysis of paintings and drawings: An introduction to the literature. In International Conference on Computer Analysis of Images and Patterns (pp.9-24). Springer Berlin Heidelberg.
  5. Martinez, K. et al., 2002. Ten years of art imaging research. Proceedings of the IEEE, 90(1), pp.28-41.
  6. Pelagotti, A. et al., 2008. Multispectral imaging of paintings. IEEE Signal Processing Magazine, 25(4), pp.27-36.
  7. Phash.org., 2014. pHash.org: Home of pHash, the open source perceptual hash library. [online] Available at: http://phash.org/ [Accessed 8 Sep. 2016].
  8. Gancarczyk, J., Sobczyk, J., 2013. Data mining approach to Image feature extraction in old painting restoration. Foundations of Computing and Decision Sciences, 38(3): pp.159-174.
  9. Haralick, R. and Shapiro, L., 1992. Computer and robot vision. 2nd ed. Reading, Mass.: Addison-Wesley Pub. Co., pp.78-120.
  10. Lowe, D. G., 1999. Object recognition from local scaleinvariant features. In: Computer vision, 1999. The proceedings of the seventh IEEE international conference on (Vol. 2, pp. 1150-1157). Ieee.
  11. Tian, X. et al., 2014. Feature integration of EODH and Color-SIFT: Application to image retrieval based on codebook. Signal Processing: Image Communication, 29(4), 530-545.
  12. Yun S.U. et al., 2007. 3D scene reconstruction system with hand-held stereo cameras. In: Proceedings of 3DTV Conference, Kos Island, Greece, May 03-07, 2007.
  13. Witek, J. et al., 2014. An application of machine learning methods to structural interaction fingerprints-a case study of kinase inhibitors. Bioorganic & medicinal chemistry letters, 24(2), 580-585.
  14. Susan, S. et al., 2015. Fuzzy match index for scale-invariant feature transform (SIFT) features with application to face recognition with weak supervision. IET Image Processing, 9(11), 951-958..
  15. Koenderink, J. J., 1984. The structure of images. Biological cybernetics, Springer, 50(5), 363-370.
  16. Lindeberg, T., 1998. Feature detection with automatic scale selection. International journal of computer vision, 30(2), 79-116..
  17. Hess, R., 2010. An open-source SIFT Library. In Proceedings of the 18th ACM international conference on Multimedia, ACM. pp. 1493-1496.
  18. Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer, New York, 2nd edition.
  19. Kerns, G. J., Székely, G. J., 2006. Definetti's Theorem for Abstract Finite Exchangeable Sequences. Journal of Theoretical Probability, 19(3): 589-608.
  20. Choi, Y. S., Park, J. H., 2012. Image hash generation method using hierarchical histogram. Multimedia Tools and Applications, 61(1): 181-194.
  21. Joachims, T., 1998. Text categorization with support vector machines-learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning, Chemnitz, Berlin Germany. pp. 137- 142.
  22. Huang, C., Davis, L. S., Townshed, J. R. G., 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23, 725-749.
  23. Roy K., 2012. ART based clustering of bag-of-features for image classification. In Image and Signal Processing (CISP), 2012 5th International Congress on. IEEE.
  24. A.B. Watson, 1993. DCT Quantization Matrices Visually Optimized for Individual Images. Proc. SPIE, San Jose, CA, USA, vol. 1913, Jan. 31, 1993, pp. 202-216.
Download


Paper Citation


in Harvard Style

Sun Y. and Niu X. (2017). Oil Portrait Snapshot Classification on Mobile . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-225-7, pages 143-149. DOI: 10.5220/0006082401430149


in Bibtex Style

@conference{visapp17,
author={Yan Sun and Xiaomu Niu},
title={Oil Portrait Snapshot Classification on Mobile},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={143-149},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006082401430149},
isbn={978-989-758-225-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2017)
TI - Oil Portrait Snapshot Classification on Mobile
SN - 978-989-758-225-7
AU - Sun Y.
AU - Niu X.
PY - 2017
SP - 143
EP - 149
DO - 10.5220/0006082401430149