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Image completion using prediction concept via support vector regression

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Abstract

Image completion is a widely used method for automatically removing objects or repairing the damaged portions of an image. However, information of the original image is often lacking in reconstructed structures; therefore, images with complex structures are difficult to restore. This study proposes a prediction-oriented image completion mechanism (PICM), which applies the prediction concept to image completion using numerous techniques and methods. The experiment results indicate that under normal circumstances, our PICM not only produces good inpainting quality but it is also easy to use.

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References

  1. Shen J., Jin X., Zhou C., Wang C.C.L.: Gradient based image completion by solving the Poisson equation. Comput. Graph. 31(1), 119–126 (2007)

    Article  Google Scholar 

  2. Hays J., Efros A.A.: Scene completion using millions of photographs. Commun. ACM 51(10), 87–94 (2008)

    Article  Google Scholar 

  3. Fang C.W., Lien J.J.J.: Rapid image completion system using multiresolution patch-based directional and nondirectional approaches. IEEE Trans. Image Process. 18(12), 2769–2779 (2009)

    Article  MathSciNet  Google Scholar 

  4. Bertalmio M., Vese L., Sapiro G., Osher S.: Simultaneous structure and texture image inpainting. IEEE Trans. Image Process. 12(8), 882–889 (2003)

    Article  Google Scholar 

  5. Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: Paper presented at the Proceedings of the 28th annual conference on Computer graphics and interactive techniques (2001)

  6. Jia, J., Tang, C.-K.: Image repairing: robust image synthesis by adaptive ND tensor voting. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Proceedings, 18–20 June 2003, vol. 641, pp. I-643–I-650

  7. Hsu H.J., Jhing-Fa W., Shang-Chia L.: A hybrid algorithm with artifact detection mechanism for region filling after object removal from a digital photograph. IEEE Trans. Image Process. 16(6), 1611–1622 (2007)

    Article  MathSciNet  Google Scholar 

  8. Li X., Hu B., Du R.: Predicting the parts weight in plastic injection molding using least squares support vector regression. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 38(6), 827–833 (2008)

    Article  Google Scholar 

  9. Qiu S., Lane T.: A framework for multiple kernel support vector regression and its applications to sirna efficacy prediction. IEEE/ACM Trans. Comput. Biol. Bioinf. 6(2), 190–199 (2009)

    Article  Google Scholar 

  10. Lee S.W., Kim D.S., Na M.G.: Prediction of DNBR using fuzzy support vector regression and uncertainty analysis. IEEE Trans. Nuclear Sci. 57(3), 1595–1601 (2010)

    Article  Google Scholar 

  11. Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Paper presented at the Proceedings of the 27th annual conference on Computer graphics and interactive techniques (2000)

  12. Perez P., Gangnet M., Blake A.: Poisson image editing. ACM Trans. Graph. 22(3), 313–318 (2003)

    Article  Google Scholar 

  13. Drori I., Cohen-Or D., Yeshurun H.: Fragment-based image completion. ACM Trans. Graph. 22(3), 303–312 (2003)

    Article  Google Scholar 

  14. Criminisi A., Perez P., Toyama K.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)

    Article  Google Scholar 

  15. Sun J., Yuan L., Jia J., Shum H.-Y.: Image completion with structure propagation. ACM Trans. Graph. 24(3), 861–868 (2005)

    Article  Google Scholar 

  16. Komodakis N., Tziritas G.: Image completion using efficient belief propagation via priority scheduling and dynamic pruning. IEEE Trans. Image Process. 16(11), 2649–2661 (2007)

    Article  MathSciNet  Google Scholar 

  17. Xu Z., Sun J.: Image inpainting by patch propagation using patch sparsity. IEEE Trans. Image Process. 19(5), 1153–1165 (2010)

    Article  MathSciNet  Google Scholar 

  18. Cortes C., Vapnik V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  19. Canny J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 679–698 (1986)

    Article  Google Scholar 

  20. Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, New York (2001)

  21. Kwatra V., Schodl A., Essa I., Turk G., Bobick A.: Graphcut textures: image and video synthesis using graph cuts. ACM Trans. Graph. 22(3), 277–286 (2003)

    Article  Google Scholar 

  22. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision ICCV 2001, vol. 2, pp. 416–423. Barcelona, Spain (2001)

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Correspondence to Yuh-Min Chen.

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Shih, CW., Lai, TH., Chu, HC. et al. Image completion using prediction concept via support vector regression. Machine Vision and Applications 24, 753–768 (2013). https://doi.org/10.1007/s00138-012-0438-0

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  • DOI: https://doi.org/10.1007/s00138-012-0438-0

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