Advertisement

Modelling Sparse Saliency Maps on Manifolds: Numerical Results and Applications

  • Euardo Alcaín
  • Ana Isabel MuñozEmail author
  • Iván Ramírez
  • Emanuele Schiavi
Chapter
Part of the SEMA SIMAI Springer Series book series (SEMA SIMAI, volume 18)

Abstract

Saliency detection is an image processing task which aims at automatically estimating visually salient object regions in a digital image mimicking human visual attention and eyes fixation. A number of different computational approaches for visual saliency estimation has recently appeared in Computer and Artificial Vision. Relevant and new applications can be found everywhere varying from automatic image segmentation and understanding, localization and quantification for biomedical and aerial images to fast video tracking and surveillance. In this contribution, we present a new variational model on finite dimensional manifolds generated by some characteristic features of the data. A Primal-Dual method is implemented for the numerical resolution showing promising preliminary results.

Keywords

Saliency detection and segmentation Superpixels Non local total variation on graphs Energy minimization Primal-dual algorithm 

Notes

Acknowledgements

This research was partially supported by projects TIN2015-69542-C2-1-R and MTM2014-57158-R.

References

  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Suesstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012). http://dx.doi.org/10.1109/TPAMI.2012.120 CrossRefGoogle Scholar
  2. 2.
    Belongie, S., Carson, C., Greenspan, H., Malik, J.: Color- and texture-based image segmentation using EM and its application to content-based image retrieval. In: IEEE International Conference on Computer Vision (ICCV) (1998). http://dl.acm.org/citation.cfm?id=938978.939161
  3. 3.
    Belyaev, A.: On Implicit Image Derivatives and Their Applications (2012). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.413.634
  4. 4.
    Chambolle, A., Pock, T.: A first order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40, 120–145 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Cheng, M., Mitra, N., Huang, X., Hu, S.: SalientShape: group saliency in image collections. Vis. Comput. 30, 443–453 (2014). http://dx.doi.org/10.1007/s00371-013-0867-4 CrossRefGoogle Scholar
  6. 6.
    Cheng, M., Mitra, N., Huang, X., Torr, P., Hu, S.: Global contrast based salient region detection. In: IEEE TPAMI (2015). http://mmcheng.net/salobj/
  7. 7.
    Christopoulos, C., Skodras, A., Ebrahimi, T.: The JPEG2000 still image coding system: an overview. IEEE Trans. Consum. Electron. 46, 1103–1127 (2000) http://ieeexplore.ieee.org/document/920468/ CrossRefGoogle Scholar
  8. 8.
    Dollár, P., Lawrence Zitnick, C.: Fast edge detection using structured forests. IEEE Trans. Pattern Anal. Mach. Intell. 37, 1558–1570 (2015). http://dblp.uni-trier.de/rec/bib/journals/pami/DollarZ15 CrossRefGoogle Scholar
  9. 9.
    Elmoataz, A., Lezoray, O., Bougleux, S.: Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing. IEEE Trans. Image Process. 17, 1047–1060 (2008)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gilboa, G., Osher, S.: Nonlocal operators with applications to image processing. SIAM Multiscale Mod. Simul. (MMS) 7, 1005–1028 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Martín, A., Garamendi, J.F., Schiavi, E.: Two efficient primal-dual algorithms for MRI rician denoising. In: Computational Modelling of Objects Represented in Images III, pp. 291–296 (2013). http://10.1201/b12753-54 Google Scholar
  12. 12.
    Rémi, G., Vinh-Thong, T., Papadakis, N.: SCALP: superpixels with contour adherence using linear path. In: 23rd International Conference on Pattern Recognition (ICPR 2016), Cancún, México (2016). https://hal.archives-ouvertes.fr/hal-01349569
  13. 13.
    Rother, C., Kolmogorov, V., Blake, A.: GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. (2004). http://doi.acm.org/10.1145/1015706.1015720
  14. 14.
    Santella, A., Agrawala, M., DeCarlo, D., Salesin, D., Cohen, M.: Gaze-based interaction for semi-automatic photo cropping. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 771–780 (2006). http://doi.acm.org/10.1145/1124772.1124886
  15. 15.
    Wang, Y., Liu, R., Song, X., Zhixun, S.: Saliency detection via nonlocal L 0 minimization. In: Computer Vision ACCV 2014. Lecture Notes in Computer Vision, vol. 9004, pp. 521–535. Springer, New York (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Euardo Alcaín
    • 1
  • Ana Isabel Muñoz
    • 1
    Email author
  • Iván Ramírez
    • 1
  • Emanuele Schiavi
    • 1
  1. 1.Departamento de Matemática Aplicada, Ciencia e Ingeniería de Materiales y Tecnología ElectrónicaUniversidad Rey Juan Carlos, ESCET, MóstolesMadridSpain

Personalised recommendations