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Visual Saliency for the Visualization of Digital Paintings

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Visual Content Indexing and Retrieval with Psycho-Visual Models

Part of the book series: Multimedia Systems and Applications ((MMSA))

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Abstract

Over the last 15 years, several applications have been developed for digital cultural heritage in the image processing and particularly in the area of digital painting. In order to help preserve cultural heritage, this chapter proposes several applications for digital paintings such as restoration, authentication, style analysis and visualization. For the visualization of digital paintings we present specific methods to visualize digital paintings based on visual saliency and in particular we propose an automatic digital painting visualization method based on visual saliency. The proposed system consists of extracting regions of interest (ROI) from a digital painting to characterize them. These close-ups are then animated on the basis of the paintings characteristics and the artist’s or designer’s aim. In order to obtain interesting results from short video clips, we developed a visual saliency map-based method. The experimental results show the efficiency of our approach and an evaluation based on a Mean Opinion Score validates our proposed method.

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References

  1. Aziz, M.Z., Mertsching, B.: Fast and robust generation of feature maps for region-based visual attention. IEEE Trans. Image Process. 17(5), 633–644 (2008)

    Article  MathSciNet  Google Scholar 

  2. Berezhnoy, I.J., Brevdo, E., Hughes, S.M., Daubechies, I., Li, J., Postma, E., Johnson, C.R., Hendriks, E., Wang, J.Z.: Image processing for artist identification. IEEE Signal Process. Mag. 25(4), 37–48 (2008)

    Article  Google Scholar 

  3. Brevdo, E., Hughes S., Brasoveanu, A., Jafarpour, S., Polatkan, G., Daubechies, I.: Stylistic analysis of paintings using wavelets and machine learning. In: Proceedings of the 17th European Signal Processing Conference (EUSIPCO), Glasgow, Scotland (2009)

    Google Scholar 

  4. Condorovici, R.G., Vranceanu, R., Vertan, C.: Saliency map retrieval for artistic paintings inspired from human understanding. In: Proceedings of SPAMEC (2011)

    Google Scholar 

  5. Corsini, M., De Rosa, A., Cappellini, V., Barni, M., Piva, A.: Artshop: an art-oriented image processing tool for cultural heritage applications. J. Vis. Comput. Animat. 14, 149–158 (2003)

    Article  Google Scholar 

  6. Druon, S., Comby, F.: La Joconde – Essai Scientifique. Extraction des craquelures, Christian Lahanier, pp. 179–184 (2007)

    Google Scholar 

  7. Gallo, G., Stanco, F., Battiato, S.: Digital Imaging for Cultural Heritage Preservation: Analysis, Restoration and Reconstruction of Ancient Artworks. Taylor and Francis, Boca Raton, FL (2011)

    Google Scholar 

  8. Giakoumis, I., Nikolaidis, N., Pitas, I.: Digital image processing techniques for the detection and removal of cracks in digitized paintings. IEEE Trans. Image Process. 15(1), 178–188 (2006)

    Article  Google Scholar 

  9. Greenspan, H., Belongie, S., Goodman, R., Perona, P., Rakshit, S., Anderson, C.H.: Overcomplete steerable pyramid filters and rotation invariance. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 222–228 (1994)

    Google Scholar 

  10. Guo, C., Zhang, L.: A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression. IEEE Trans. Image Process. 19(1), 185–198 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  11. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)

    Article  Google Scholar 

  12. Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proceedings of NIPS, pp. 545–552 (2006)

    Google Scholar 

  13. Itti, L.: Models of bottom-up and top-down visual attention. Ph.D. thesis, California Institute of Technology (2000)

    Google Scholar 

  14. Itti, L., Koch, C.: A comparison of feature combination strategies for saliency-based visual attention systems. J. Electron. Imaging 10, 161–169 (1999)

    Article  Google Scholar 

  15. Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

    Article  Google Scholar 

  16. ITU-T Recommendation (P.910): Subjective video quality assessment methods for multimedia applications (2000)

    Google Scholar 

  17. Kennel, P., Puech, W., Comby, F.: Visualization framework of digital paintings based on visual saliency for cultural heritage. Multimedia Tools Appl. 76(1), 561–575 (2017)

    Article  Google Scholar 

  18. Khan, F.S., Beigpour, S., Weijer, J., Felsberg, M.: Painting-91: a large scale database for computational painting categorization. Mach. Vis. Appl. 25(6), 1385–1397 (2014)

    Article  Google Scholar 

  19. Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4, 219–227 (1985)

    Google Scholar 

  20. Li, J., Wang, J.Z.: Studying digital imagery of ancient paintings by mixtures of stochastic models. IEEE Trans. Image Process. 13(3), 340 (2004)

    Article  Google Scholar 

  21. Lu, L.-C., Shih, T.K., Chang, R.-C., Huang, H.-C.: Multi-layer inpainting on Chinese artwork. In: Proceedings of IEEE International Conference on Multimedia and Expo (ICME) (2004)

    Google Scholar 

  22. McNamara, A., Booth, T., Sridharan, S., Caffey, S., Grimm, C., Bailey, R.: Directing gaze in narrative art. In: Proceedings of the ACM Symposium on Applied Perception, SAP’12, pp. 63–70. Association for Computing Machinery, New York (2012)

    Google Scholar 

  23. Niebur, E.: Saliency map. Scholarpedia 2(8), 2675 (2007)

    Article  Google Scholar 

  24. Oncu, A.I., Deger, F., Hardeberg, J.Y.: Evaluation of Digital Inpainting Quality in the Context of Artwork Restoration, pp. 561–570. Springer, Heidelberg (2012)

    Google Scholar 

  25. Oyeka, A., Ebuh, G.: Modified Wilcoxon signed-rank test. Open J. Stat. 2, 172–176 (2012)

    Article  MathSciNet  Google Scholar 

  26. Pei, S.-C., Ding, J.-.J., Chang, J.-H.: Efficient implementation of quaternion fourier transform, convolution, and correlation by 2-d complex FFT. IEEE Trans. Signal Process. 49(11), 2783–2797 (2001)

    Google Scholar 

  27. Pitzalis, D., Aitken, G., Autrusseau, F., Babel, M., Cayre, F., Puech, W.: TSAR: secure transfer of high resolution art images. In: Proceedings of EVA’08 Florence, Electronic Imaging and the Visual Art, France (2008)

    Google Scholar 

  28. Platia, L., Cornelis, B., Ruic, T., Piurica, A., Dooms, A., Martens, M., De Mey, M., Daubechies, I.: Spatiogram features to characterize pearls in paintings. In: Proceedings of IEEE ICIP’2011 (2011)

    Google Scholar 

  29. Postma, E.O., Berezhnoy, I.E., van den Herik, H.J.: Authentic: computerized brushstroke analysis. In: Proceedings of the 2005 IEEE International Conference on Multimedia and Expo, ICME, Amsterdam, The Netherlands, 6–9 July 2005

    Google Scholar 

  30. Quiroga, R.Q., Pedreira, C.: How do we see art: an eye-tracker study. Front. Hum. Neurosci. 5, 98 (2011)

    Article  Google Scholar 

  31. Redies, C., Fuchs, I., Ansorge, U., Leder, H.: Salience in paintings: bottom-up influences on eye fixations. Cogn. Comput. 3(1), 25–36 (2011)

    Article  Google Scholar 

  32. Shida, B., van de Weijer, J., Khan, F.S., Felsberg, M.: Painting-91: a large scale database for computational painting categorization. Mach. Vis. Appl. 25, 1385–1397 (2014)

    Article  Google Scholar 

  33. van der Maaten, L., Postma, E.: Identifying the real van gogh with brushstroke textons. White paper, Tilburg University, Feb 2009

    Google Scholar 

  34. Vertan, C., Condorovici, R.G., Vrânceanu, R.: Saliency map retrieval for artistic paintings inspired from human understanding. In: Proceedings of SPAMEC 2011, Cluj-Napoca, Romania, pp. 101–104 (2011)

    Google Scholar 

  35. Wolfe, J.M., Horowitz, T.S.: What attributes guide the deployment of visual attention and how do they do it? Nat. Rev. Neurol. 5(6), 495–501 (2004)

    Article  Google Scholar 

  36. Wu, B., Xu, L., Zeng, L., Wang, Z., Wang, Y.: A unified framework for spatiotemporal salient region detection. EURASIP J. Image Video Process. 2013(1), 16 (2013)

    Article  Google Scholar 

  37. Xu, L., Li, H., Zeng, L., Ngan, K.N.: Saliency detection using joint spatial-color constraint and multi-scale segmentation. J. Vis. Commun. Image Represent. 24(4), 465–476 (2013)

    Article  Google Scholar 

  38. Yang, S., Cheung, G., Le Callet, P., Liu, J., Guo, Z.: Computational modeling of artistic intention: quantify lighting surprise for painting analysis. In: Proceedings of the Eighth International Conference on Quality of Multimedia Experience (2016)

    Google Scholar 

  39. Zeng, Y.-C., Pei, S.-C., Chang, C.-H.: Virtual restoration of ancient Chinese paintings using color contrast enhancement and lacuna texture synthesis. IEEE Trans. Image Process. (Special Issue on Image Processing for Cultural Heritage) 13(3), 416–429 (2004)

    Google Scholar 

  40. Zöllner, F.: Leonardo’s Portrait of Mona Lisa del Giocondo. Gazette des Beaux-Arts 121(1), 115–138 (1993)

    Google Scholar 

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Acknowledgements

The authors would like to thank volunteers who accepted to participate in our opinion score campaign.

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Authors and Affiliations

  1. IMFT, INPT/Univ. Toulouse, Toulouse, France

    Pol Kennel

  2. LIRMM, CNRS/Univ. Montpellier, Montpellier, France

    Frédéric Comby & William Puech

Authors
  1. Pol Kennel
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  2. Frédéric Comby
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  3. William Puech
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Corresponding author

Correspondence to William Puech .

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Editors and Affiliations

  1. LaBRI UMR 5800, Univ. Bordeaux, CNRS, Bordeaux INP, Univ. Bordeaux, Talence, France

    Jenny Benois-Pineau

  2. LS2N, UMR CNRS 6004, Université de Nantes, Nantes Cedex 3, France

    Patrick Le Callet

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Kennel, P., Comby, F., Puech, W. (2017). Visual Saliency for the Visualization of Digital Paintings. In: Benois-Pineau, J., Le Callet, P. (eds) Visual Content Indexing and Retrieval with Psycho-Visual Models. Multimedia Systems and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-57687-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-57687-9_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57686-2

  • Online ISBN: 978-3-319-57687-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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