Skip to main content
SpringerLink
Log in

Automated co-superpixel generation via graph matching

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this paper, a novel ‘co-superpixel’ generation method is proposed via the graph matching. The co-superpixel can capture the common semantic information in coupled images. Therefore, it is significant for various applications in visual pattern recognition. Specifically, we first introduce a superpixel correspondence method based on the graph matching. The main property is that it has the ability to capture the consistent intermediate-level semantic information in coupled images, which can represent the region-based similarity rather than the conventional similarity based on low-level vision features. Second, a new co-superpixel generation method is proposed by the superpixel-merging incorporated with the graph matching cost and the adjacent superpixel appearance similarity in coupled images simultaneously. Furthermore, we extend the proposed co-superpixel method to tackle the object matching problem. The experimental results show that the object matching can be effectively addressed by the co-superpixel. The proposed method is effective for challenging cases in which object appearance changes, deformation and background clutter.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Arbelaez, P., Hariharan, B., Gu, C., Gupta, S., Bourdev, L., Malik, J.: Semantic segmentation using regions and parts. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3378–3385 (2012)

  2. Batra, D., Kowdle, A., Parikh, D., Luo, J., Chen, T.: iCoseg: Interactive co-segmentation with intelligent scribble guidance. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3169–3176 (2010)

  3. Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary amp; region segmentation of objects in n-d images. In: IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 105–112 (2001)

  4. Chen, Y., Chan, A., Wang, G.: Adaptive figure-ground classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 654–661 (2012)

  5. Cho, M., Lee, J., Lee, K.M.: Reweighted random walks for graph matching. In: European Conference on Computer Vision (ECCV), pp. 492–505 (2010)

  6. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Article  Google Scholar 

  7. Duchenne, O., Bach, F., Kweon, I., Ponce, J.: A tensor-based algorithm for high-order graph matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1980–1987 (2009)

  8. Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. (2010)

  9. Filho, C.F.F.C., Pinheiro, C.F.M., Costa, M.G.F., de Albuquerque Pereira, W.C.: Applying a novelty filter as a matching criterion to iris recognition for binary and real-valued feature vectors. Signal Image Video Process. 7(2), 287–296 (2013)

    Article  Google Scholar 

  10. Fisher, R.: The correlation between relatives on the supposition of mendelian inheritance. Trans. Roy. Soc. Edinb. 52, 399–433 (1918)

    Article  Google Scholar 

  11. Fragkiadaki, K., Zhang, G., Shi, J.: Video segmentation by tracing discontinuities in a trajectory embedding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1846–1853 (2012)

  12. Freedman, D.: An improved image graph for semi-automatic segmentation. Signal Image Video Process. (2012). doi:10.1007/s11760-010-0181-9

  13. Fulkerson, B., Vedaldi, A., Soatto, S.: Class segmentation and object localization with superpixel neighborhoods. In: IEEE International Conference on Computer Vision (ICCV), pp. 670–677 (2009)

  14. Hadid, A., Dugelay, J.L., Pietikainen, M.: On the use of dynamic features in face biometrics: recent advances and challenges. Signal Image Video Process. 5, 495–506 (2011)

    Google Scholar 

  15. Ji, R., Yao, H., Liang, D.: Drm: dynamic region matching for image retrieval using probabilistic fuzzy matching and boosting feature selection. Signal Image Video Process. 2(1), 59–71 (2008)

    Article  MATH  Google Scholar 

  16. Joulin, A., Bach, F., Ponce, J.: Multi-class cosegmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 542–549 (2012)

  17. Kaur, A., Singh, C.: Automatic cephalometric landmark detection using Zernike moments and template matching. Signal Image Video Process. (2013). doi:10.1007/s11760-013-0432-7

  18. Khan, A., Ullah, J., Jaffar, M.A., Choi, T.S.: Color image segmentation: a novel spatial fuzzy genetic algorithm. Signal Image Video Process. (2012). doi:10.1007/s11760-012-0347-8

  19. Kuang, Z., Schnieders, D., Zhou, H., Wong, K.Y.K., Yu, Y., Peng, B.: Learning image-specific parameters for interactive segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 590–597 (2012)

  20. Kuettel, D., Ferrari, V.: Figure-ground segmentation by transferring window masks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 558–565 (2012)

  21. Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 1482–1489 Vol. 2 (2005)

  22. Levinshtein, A., Stere, A., Kutulakos, K., Fleet, D., Dickinson, S., Siddiqi, K.: Turbopixels: Fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2290–2297 (2009)

    Article  Google Scholar 

  23. Li, H., Ngan, K.: Saliency model based face segmentation in head-and-shoulder video sequences. J. Vis. Commun. Image Represent. 19(5), 320–333 (2008)

    Article  Google Scholar 

  24. Li, H., Ngan, K.: Learning to extract focused objects from low dof images. IEEE Trans. Circuits Syst. Video Technol. 21(11), 1571–1580 (2011)

    Article  MATH  Google Scholar 

  25. Li, H., Ngan, K.N.: A co-saliency model of image pairs. IEEE Trans. Image Process. 20(12), 3365–3375 (2011)

    Article  MathSciNet  Google Scholar 

  26. Li, Z., Wu, X., Chang, S.: Segmentation using superpixels: A bipartite graph partitioning approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 789–796 (2012)

  27. Meng, F., Li, H., Liu, G., Ngan, K.N.: Object co-segmentation based on shortest path algorithm and saliency model. IEEE Trans. Multimed. 14(5), 1429–1441 (2012)

    Article  Google Scholar 

  28. Meng, F., Li, H., Liu, G., Ngan, K.N.: Form logo to object segmentation. IEEE Trans. Multimed. 15(8), 2186–2197 (2013)

    Google Scholar 

  29. Moore, A., Prince, S., Warrell, J., Mohammed, U., Jones, G.: Superpixel lattices. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)

  30. Naikal, N., Yang, A., Sastry, S.: Towards an efficient distributed object recognition system in wireless smart camera networks. In: 13th Conference on Information Fusion (FUSION), pp. 1–8 (2010)

  31. Ning, J., Zhang, L., Zhang, D., Wu, C.: Interactive image segmentation by maximal similarity based region merging. Pattern Recogn. 43(2), 445–456 (2010)

    Article  MATH  Google Scholar 

  32. Paulhac, L., Makris, P., Ramel, J.Y., Gregoire, J.M.: A framework of perceptual features for the characterisation of 3D textured images. Signal Image Video Process. (2013). doi:10.1007/s11760-013-0438-1

  33. Shi, J., Malik, J.: Normalized cuts and image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 731–737 (1997)

  34. Shotton, J., Winn, J., Rother, C., Criminisi, A.: Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Proceedings of European Conference Computer Vision (ECCV), pp. 1–15 (2006)

  35. Tighe, J., Lazebnik, S.: Superparsing: scalable nonparametric image parsing with superpixels. In: European Conference on Computer Vision (ECCV), pp. 352–365 (2010)

  36. Tupin, F., Roux, M.: Markov random field on region adjacency graph for the fusion of SAR and optical data in radargrammetric applications. IEEE Trans. Geosci. Remote Sens. 43(8), 1920–1928 (2005)

    Article  Google Scholar 

  37. Vazquez-Reina, A., Avidan, S., Pfister, H., Miller, E.: Multiple hypothesis video segmentation from superpixel flows. In: European Conference on Computer Vision (ECCV), pp. 268–281 (2010)

  38. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)

    Article  Google Scholar 

  39. Xu, C., Corso, J.: Evaluation of super-voxel methods for early video processing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1202–1209 (2012)

  40. Xu, L., Zeng, L., Wang, Z.: Saliency-based superpixels. Signal Image Video Process. (2013). doi:10.1007/s11760-013-0520-8

  41. Yu, Y., Huang, K., Chen, W., Tan, T.: A novel algorithm for view and illumination invariant image matching. IEEE Trans. Image Process. 21(1), 229–240 (2012)

    Google Scholar 

  42. Zass, R., Shashua, A.: Probabilistic graph and hypergraph matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2008)

  43. Zhang, L., Ji, Q.: A bayesian network model for automatic and interactive image segmentation. IEEE Trans. Image Process. 20(9), 2582–2593 (2011)

    Article  MathSciNet  Google Scholar 

  44. Zhang, W.J., Feng, X.C., Han, Y.: A novel image segmentation model with an edge weighting function. Signal Image Video Process. (2013). doi:10.1007/s11760-013-0495-5

  45. Zhou, F., De La Torre, F.: Factorized graph matching. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 127–134 (2012)

Download references

Acknowledgments

This work was partially supported by NSFC (No.61179060, and 61101091), and Fundamental Research Funds for the Central Universities (ZYGX2012J019).

Author information

Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Yurui Xie, Lingfeng Xu & Zhengning Wang

Authors
  1. Yurui Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lingfeng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhengning Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yurui Xie.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xie, Y., Xu, L. & Wang, Z. Automated co-superpixel generation via graph matching. SIViP 8, 753–763 (2014). https://doi.org/10.1007/s11760-013-0589-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-013-0589-0

Keywords

Search

Navigation