Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Spline-Based Dense Medial Descriptors for Image Simplification Using Saliency Maps

  • Conference paper
  • First Online:
Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1691))

  • 340 Accesses

Abstract

Medial descriptors have attracted increasing interest in image representation, simplification, and compression. Recently, such descriptors have been separately used to (a) increase the local quality of representing salient features in an image and (b) globally compress an entire image via a B-spline encoding. To date, the two desiderates, (a) high local quality and (b) high overall compression of images, have not been addressed by a single medial method. We achieve this integration by presenting Spatial Saliency Spline Dense Medial Descriptors (3S-DMD) for saliency-aware image simplification-and-compression. Our method significantly improves the trade-off between compression and image quality of earlier medial-based methods while keeping perceptually salient features. We also demonstrate the added-value of user-designed, as compared to automatically-computed, saliency maps. We show that our method achieves both higher compression and better quality than JPEG for a broad range of images and, for specific image types, yields higher compression and similar quality than JPEG 2000.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Agustsson, E., Tschannen, M., Mentzer, F., Timofte, R., Van Gool, L.: Generative adversarial networks for extreme learned image compression. In: ICCV, pp. 221–231 (2019)

    Google Scholar 

  2. Alaei, A., Raveaux, R., Conte, D.: Image quality assessment based on regions of interest. Signal Image Video Process. 11, 673–680 (2017)

    Article  Google Scholar 

  3. Andrushia, A., Thangarjan, R.: Saliency-based image compression using Walsh and Hadamard transform. In: Lect Notes Comp Vision Biomech, pp. 21–42 (2018)

    Google Scholar 

  4. Attali, D., Montanvert, A.: Computing and simplifying 2D and 3D continuous skeletons. Comput. Vision Image Understand. 67(3), 261–273 (1997)

    Article  Google Scholar 

  5. Ballard, F.: Better portable graphics (2018). https://bellard.org/bpg

  6. Borji, A., Cheng, M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE TIP 24(12), 5706–22 (2015)

    MATH  Google Scholar 

  7. Cao, T.T., Tang, K., Mohamed, A., Tan, T.S.: Parallel banding algorithm to compute exact distance transform with the GPU. In: Proceedings ACM I3D, pp. 83–90 (2010)

    Google Scholar 

  8. Chen, T., Cheng, M., Tan, P., Shamir, A., Hu, S.: Sketch2photo: Internet image montage. ACM TOG 28(5) (2009)

    Google Scholar 

  9. Cheng, M.: MSRA10K salient object database (2014). mmcheng.net/msra10k

    Google Scholar 

  10. Cheng, M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.: Global contrast based salient region detection. IEEE TPAMI 37(3), 569–582 (2014)

    Article  Google Scholar 

  11. Choi, Y., El-Khamy, M., Lee, J.: Variable rate deep image compression with a conditional autoencoder. ICCV pp. 3146–3154 (2019)

    Google Scholar 

  12. Daintith, J., Wright, E.: A Dictionary of Computing. Oxford Univ, Press (2008)

    Book  MATH  Google Scholar 

  13. Eberly, D.: Least-squares fitting of data with B-spline curves (2014). geometric Tools. www.geometrictools.com/Documentation/BSplineCurveLeastSquaresFit.pdf

    Google Scholar 

  14. Engelke, U., Le Callet, P.: Perceived interest and overt visual attention in natural images. Image Commun. 39(PB), 386–404 (2015)

    Google Scholar 

  15. Fabbri, R., Costa, L.D.F., Torelli, J.C., Bruno, O.M.: 2D Euclidean distance transform algorithms: a comparative survey. ACM Comput Surv 40(1), 1–44 (2008)

    Article  Google Scholar 

  16. Falcão, A., Bragantini, J.: The role of optimum connectivity in image segmentation: can the algorithm learn object information during the process? In: Couprie, M., Cousty, J., Kenmochi, Y., Mustafa, N. (eds.) DGCI 2019. LNCS, vol. 11414, pp. 180–194. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-14085-4_15

    Chapter  Google Scholar 

  17. Falcão, A., Stolfi, J., Lotufo, R.: The image foresting transform: theory, algorithms, and applications. IEEE TPAMI 26, 19–29 (2004)

    Article  Google Scholar 

  18. Goferman, S., Zelnik, L., Tal, A.: Context-aware saliency detection. IEEE TPAMI 34(10), 1915–1926 (2011)

    Article  Google Scholar 

  19. Hesselink, W.H., Roerdink, J.B.T.M.: Euclidean skeletons of digital image and volume data in linear time by the integer medial axis transform. IEEE TPAMI 30(12), 2204–2217 (2008)

    Article  Google Scholar 

  20. Jiang, P., Ling, H., Yu, J., Peng, J.: Salient region detection by UFO: Uniqueness, focusness and objectness. In: Proceedings of the ICCV, pp. 1976–1983 (2013)

    Google Scholar 

  21. Kimmel, R., Shaked, D., Kiryati, N., Bruckstein, A.M.: Skeletonization via distance maps and level sets. CVIU 62(3), 382–391 (1995)

    Google Scholar 

  22. Kresch, R., Malah, D.: Skeleton-Based morphological coding of binary images. IEEE TIP 7(10), 1387–1399 (1998)

    MATH  Google Scholar 

  23. Lam, L., Lee, S., Suen, C.Y.: Thinning methodologies - a comprehensive survey. IEEE TPAMI 14(9), 869–885 (1992)

    Article  Google Scholar 

  24. Le Callet, P., Niebur, E.: Visual attention and applications in multimedia technologies. Proc. IEEE 101(9), 2058–2067 (2013)

    Article  Google Scholar 

  25. Li, X., Lu, H., Zhang, L., Ruan, X., Yang, M.: Saliency detection via dense and sparse reconstruction. In: Proceedings of the ICCV, pp. 2976–2983 (2013)

    Google Scholar 

  26. Liu, H., Engelke, U., Wang, J., Callet, Le, P., Heynderickx, I.: How does image content affect the added value of visual attention in objective image quality assessment? IEEE Signal Proc. Let. 20, 355–358 (2013)

    Google Scholar 

  27. Liu, H., Heynderickx, I.: Visual attention in objective image quality assessment: Based on eye-tracking data. IEEE TCSVT 21(7), 971–982 (2011)

    Google Scholar 

  28. Meijster, A., Roerdink, J., Hesselink, W.: A general algorithm for computing distance transforms in linear time. In: Proceedings ISMM, pp. 331–340 (2002)

    Google Scholar 

  29. de Melo Joao, L., de Castro Belem, F., Falcao, A.X.: Itself: Iterative saliency estimation flexible framework. Available at https://arxiv.org/abs/2006.16956 (2020)

  30. Movahedi, V., Elder, J.: Design and perceptual validation of performance measures for salient object segmentation. In: IEEE Computer Society Conference (2010)

    Google Scholar 

  31. Ogniewicz, R., Kübler, O.: Hierarchical voronoi skeletons. Patt. Recogn. 28(3), 343–359 (1995)

    Article  Google Scholar 

  32. Orzan, A., Bousseau, A., Barla, P., Winnemöller, H., Thollot, J., Salesin, D.: Diffusion curves: a vector representation for smooth-shaded images. Commun. ACM 56(7), 101–108 (2013)

    Article  Google Scholar 

  33. Peng, H., Li, B., Ling, H., Hu, W., Xiong, W., Maybank, S.J.: Salient object detection via structured matrix decomposition. IEEE TPAMI 39(4), 818–832 (2016)

    Article  Google Scholar 

  34. Piegl, L., Tiller, W.: The NURBS Book (2nd Ed.). Springer-Verlag (1997). https://doi.org/10.1007/978-3-642-59223-2

  35. Pizer, S., Siddiqi, K., Székely, G., Damon, J., Zucker, S.: Multiscale medial loci and their properties. IJCV 55, 155–179 (2003)

    Article  MATH  Google Scholar 

  36. Saha, P.K., Borgefors, G., Sanniti di Baja, G.: A survey on skeletonization algorithms and their applications. Patt. Recogn. Lett. 76, 3–12 (2016)

    Google Scholar 

  37. Shi, J., Yan, Q., Xu, L., Jia, J.: Hierarchical image saliency detection on extended CSSD. IEEE TPAMI 38(4D) (2016)

    Google Scholar 

  38. Siddiqi, K., Pizer, S.: Medial representations: mathematics, algorithms and applications (1nd Ed.). Springer (2008). https://doi.org/10.1007/978-1-4020-8658-8

  39. Taubman, D.S., Marcellin, M.W.: JPEG 2000: Image compression fundamentals, standards and practice. Kluwer Academic Publishers (2001)

    Google Scholar 

  40. Telea, A.: Feature preserving smoothing of shapes using saliency skeletons. In: Proc. VMLS, pp. 153–170 (2012)

    Google Scholar 

  41. Telea, A.: CUDASkel: real-time computation of exact Euclidean multiscale skeletons on CUDA (2019). webspace.science.uu.nl/\(\sim \)telea001/Shapes/CUDASkel

    Google Scholar 

  42. Telea, A., Wijk, van, J.: An augmented fast marching method for computing skeletons and centerlines. In: Eurographics, pp. 251–259 (2002)

    Google Scholar 

  43. Toderici, G., et al.: Variable rate image compression with recurrent neural networks. In: 4th ICLR (2016)

    Google Scholar 

  44. Wallace, G.K.: The JPEG still picture compression standard. IEEE TCE 38(1), xviii-xxxiv (1992)

    Google Scholar 

  45. Wang, H., Schuster, G.M., Katsaggelos, A.K., Pappas, T.N.: An efficient rate-distortion optimal shape coding approach utilizing a skeleton-based decomposition. IEEE TIP 12(10), 1181–1193 (2003)

    MATH  Google Scholar 

  46. Wang, J.: 3S-DMD supplementary material (2021). https://github.com/WangJieying/3S-DMD-resources

  47. Wang, J., Kosinka, J., Telea, A.: Spline-based dense medial descriptors for lossy image compression. J. Imag. 7(8), 153 (2021)

    Google Scholar 

  48. Wang, J., Kosinka, J., Telea, A.: Spline-based medial axis transform representation of binary images. Comput. Graph. 98, 165–176 (2021)

    Article  Google Scholar 

  49. Wang, J., de Melo Joao, L., Falcão, A., Kosinka, J., Telea, A.: Focus-and-context skeleton-based image simplification using saliency maps. In: Proceedings of the VISAPP, pp. 45–55. SciTePress (2021)

    Google Scholar 

  50. Wang, J., Terpstra, M., Kosinka, J., Telea, A.: Quantitative evaluation of dense skeletons for image compression. Information 11(5), 274 (2020)

    Article  Google Scholar 

  51. Wang, W., Lai, Q., Fu, H., Shen, J., Ling, H.: Salient object detection in the deep learning era: An in-depth survey. IEEE TPAMI PP (2021)

    Google Scholar 

  52. Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE TIP 13, 600–612 (2004)

    Google Scholar 

  53. Wang, Z., Bovik, A.: Mean squared error: Love it or leave it? a new look at signal fidelity measures. IEEE Signal Proc. Mag. 26, 98–117 (2009)

    Article  Google Scholar 

  54. Wang, Z., Simoncelli, E., Bovik, A.: Multiscale structural similarity for image quality assessment. In: ACSSC, pp. 1398–1402 (2003)

    Google Scholar 

  55. Yushkevich, P., Thomas Fletcher, P., Joshi, S., Thall, A., Pizer, S.M.: Continuous medial representations for geometric object modeling in 2D and 3D. Image Vision Comput. 21(1), 17–27 (2003)

    Article  Google Scholar 

  56. Zhang, J., et al.: Hypergraph optimization for salient region detection based on foreground and background queries. IEEE Access 6, 26729–267241 (2018)

    Article  Google Scholar 

  57. Zhu, Y., Sun, F., Choi, Y.K., Jüttler, B., Wang, W.: Computing a compact spline representation of the medial axis transform of a 2D shape. Graphical Models 76(5), 252–262 (2014)

    Article  Google Scholar 

  58. Zwan, M.V.D., Meiburg, Y., Telea, A.: A dense medial descriptor for image analysis. In: Proceedings of the VISAPP, pp. 285–293 (2013)

    Google Scholar 

  59. Zünd, F., Pritch, Y., Sorkine-Hornung, A., Mangold, S., Gross, T.: Content-aware compression using saliency-driven image retargeting. In: IEEE ICIP, pp. 1845–1849 (2013)

    Google Scholar 

Download references

Acknowledgments

The first author acknowledges the China Scholarship Council (Grant number: 201806320354) for financial support.

Author information

Authors and Affiliations

  1. Bernoulli Institute, University of Groningen, 9747 AG, Groningen, The Netherlands

    Jiří Kosinka

  2. Department of Information Systems, Institute of Computing, University of Campinas, São Paulo, 13083-852, Brazil

    Leonardo de Melo & Alexandre X. Falcão

  3. Department of Information and Computing Sciences, Utrecht University, 3584 CC, Utrecht, The Netherlands

    Alexandru Telea

  4. Bernoulli Institute, Shandong University of Science and Technology, 579 Qianwangang Road, Huangdao District, Qingdao, 266590, Shandong Province, P.R. China

    Jieying Wang

Authors
  1. Jieying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonardo de Melo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexandre X. Falcão
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiří Kosinka
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexandru Telea
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jieying Wang .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    A. Augusto de Sousa

  2. Czech Technical University in Prague, Prague, Czech Republic

    Vlastimil Havran

  3. Mines ParisTech, Paris, France

    Alexis Paljic

  4. Davidson College, Davidson, NC, USA

    Tabitha Peck

  5. French Civil Aviation University (ENAC), Toulouse, France

    Christophe Hurter

  6. Monash University, Melbourne, Australia

    Helen Purchase

  7. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  8. University of Barcelona, Barcelona, Spain

    Petia Radeva

  9. IRISA, University of Rennes 1, Rennes, France

    Kadi Bouatouch

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, J., Melo, L.d., Falcão, A.X., Kosinka, J., Telea, A. (2023). Spline-Based Dense Medial Descriptors for Image Simplification Using Saliency Maps. In: de Sousa, A.A., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2021. Communications in Computer and Information Science, vol 1691. Springer, Cham. https://doi.org/10.1007/978-3-031-25477-2_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-25477-2_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25476-5

  • Online ISBN: 978-3-031-25477-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation