Generalised Gradient Vector Flow for Content-Aware Image Resizing

  • Tiziana Rotondo
  • Alessandro OrtisEmail author
  • Sebastiano Battiato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11752)


Image retargeting is devoted to preserve the visual content of images with a proper resizing, removing vertical and/or horizontal paths of pixels which contain low semantic information. In this paper, a method based on the Generalised Gradient Vector Flow (GGVF) is presented. The GGVF formulation allows the balancing of the smoothing term and data term of the flow by proper parameter tuning. The proposed approach has been tested by considering a data set of 1000 images and varying the percentage of resizing from 10% to 50% and for different values of the aim involved parameter K. Results show that our algorithm better preserves the important information compared to GVF and Seam Carving approaches. Preliminary results show an underlying relation between parameter K and the percentage of resizing has been also exploited.


Image resizing Image retargeting Seam carving GGVF 


  1. 1.
    Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graph. 26(3), 10 (2007)CrossRefGoogle Scholar
  2. 2.
    Battiato, S., Farinella, G.M., Puglisi, G., Ravi, D.: Saliency-based selection of gradient vector flow paths for content aware image resizing. IEEE Trans. Image Process. 23(5), 2081–2095 (2014). Scholar
  3. 3.
    Cho, D., Park, J., Oh, T.H., Tai, Y.W., Kweon, I.: Weakly- and self-supervised learning for content-aware deep image retargeting. In: ICCV, pp. 4568–4577, October 2017.
  4. 4.
    Fang, Y., Chen, Z., Lin, W., Lin, C.: Saliency detection in the compressed domain for adaptive image retargeting. IEEE Trans. Image Process. 21(9), 3888–3901 (2012). Scholar
  5. 5.
    Fang, Y., Fang, Z., Yuan, F., Yang, Y., Yang, S., Xiong, N.N.: Optimized multioperator image retargeting based on perceptual similarity measure. IEEE Trans. Syst. Man Cybern. Syst. 47(11), 2956–2966 (2017). Scholar
  6. 6.
    Hsu, C., Lin, C., Fang, Y., Lin, W.: Objective quality assessment for image retargeting based on perceptual geometric distortion and information loss. IEEE J. Sel. Top. Sig. Process. 8(3), 377–389 (2014). Scholar
  7. 7.
    Park, H.K., Chung, M.J.: External force of snake: virtual electric field. Electron. Lett. 38, 1500–1502 (2002). Scholar
  8. 8.
    Liang, Y., Liu, Y.J., Gutierrez, D.: Objective quality prediction of image retargeting algorithms. IEEE Trans. Vis. Comput. Graph. 23(2), 1099–1110 (2016)CrossRefGoogle Scholar
  9. 9.
    Liu, C., Yuen, J., Torralba, A.: SIFT Flow: dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 978–994 (2011). Scholar
  10. 10.
    Liu, Y., Luo, X., Xuan, Y., Chen, W., Fu, X.: Image retargeting quality assessment. Comput. Graph. Forum 30(2), 583–592 (2011)CrossRefGoogle Scholar
  11. 11.
    Panozzo, D., Weber, O., Sorkine, O.: Robust image retargeting via axis-aligned deformation. Comput. Graph. Forum 31(2), 229–236 (2012)CrossRefGoogle Scholar
  12. 12.
    Rubinstein, M., Gutierrez, D., Sorkine, O., Shamir, A.: A comparative study of image retargeting. ACM Trans. Graph. 29(6), 160 (2010)CrossRefGoogle Scholar
  13. 13.
    Rubinstein, M., Shamir, A., Avidan, S.: Multi-operator media retargeting. ACM Trans. Graph. 28(3), 1–11 (2009). (Proceedings SIGGRAPH 2009)CrossRefGoogle Scholar
  14. 14.
    Xu, C., Prince, J.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7, 359–369 (1998). Scholar
  15. 15.
    Xu, C., Prince, J.: Generalized gradient vector flow external forces for active contours. Sig. Process. 71, 131–139 (2000). Scholar
  16. 16.
    Zhou, S., Lu, Y., Li, N., Wang, Y.: Extension of the virtual electric field model using bilateral-like filter for active contours. Sig. Image Video Process. (2019). Scholar
  17. 17.
    Zhou, Y., Zhang, L., Zhang, C., Li, P., Li, X.: Perceptually aware image retargeting for mobile devices. IEEE Trans. Image Process. 27(5), 2301–2313 (2018). Scholar
  18. 18.
    Zhu, S., Gao, R.: A novel generalized gradient vector flow snake model using minimal surface and component-normalized method for medical image segmentation. Biomed. Sig. Process. Control 26, 1–10 (2016). Scholar
  19. 19.
    Zhu, S., Zhou, Q., Gao, R.: A novel snake model using new multi-step decision model for complex image segmentation. Comput. Electr. Eng. 51(C), 58–73 (2016). Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of CataniaCataniaItaly

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