Classification of Segmented Objects through a Multi-net Approach

  • Alessandro Zamberletti
  • Ignazio Gallo
  • Simone Albertini
  • Marco Vanetti
  • Angelo Nodari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7477)

Abstract

The proposed model aims to extend the MNOD algorithm adding a new type of node specialized in object classification. For each potential object identified by the MNOD, a set of segments are generated using a min-cut based algorithm with different seeds configurations. These segments are classified by a suitable neural model and then the one with higher value is chosen, in agreement with a proper energy function. The proposed method allows to segment and classify each object simultaneously. The results showed in the experiment section highlight the potential and the cost of having unified segmentation and classification in a single model.

Keywords

object segmentation object classification neural networks minimum cut 

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References

  1. 1.
    Felzenszwalb, P.F., Mcallester, D.A., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proceedings of International Conference of Computer Vision and Pattern Recognition (2008)Google Scholar
  2. 2.
    Gallo, I., Nodari, A.: Learning object detection using multiple neural netwoks. In: Proceedings of International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. INSTICC Press (2011)Google Scholar
  3. 3.
    Li, F., Carreira, J., Sminchisescu, C.: Object recognition as ranking holistic figure-ground hypotheses. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1712–1719. IEEE (2010)Google Scholar
  4. 4.
    Carreira, J., Sminchisescu, C.: Constrained parametric min-cuts for automatic object segmentation. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (2010)Google Scholar
  5. 5.
    Serre, T., Wolf, L., Bileschi, S., Riesenhuber, M., Poggio, T.: Robust object recognition with cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 411–426 (2007)CrossRefGoogle Scholar
  6. 6.
    Greig, D.M., Porteous, B.T., Seheult, A.H.: Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society, 271–279 (1989)Google Scholar
  7. 7.
    Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: Proceedings of IEEE International Conference on Computer Vision, vol. 1 (2001)Google Scholar
  8. 8.
    Sharkey, A.J.: Multi-Net Systems. In: Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems. Springer (1999)Google Scholar
  9. 9.
    Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of IEEE International Conference on Neural Networks, pp. 586–591 (1993)Google Scholar
  10. 10.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  11. 11.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)CrossRefGoogle Scholar
  12. 12.
    Carreira, J., Sminchisescu, C.: CPMC: Automatic object segmentation using constrained parametric min-cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)Google Scholar
  13. 13.
    Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, CIVR 2007, pp. 401–408. ACM (2007)Google Scholar
  14. 14.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the International Conference on Computer Vision. IEEE Computer Society (1999)Google Scholar
  15. 15.
    Berg, A.C., Malik, J.: Geometric blur for template matching. In: Proceedings of International Conference on Computer Vision and Pattern Recognition, vol. 1(C), pp. 607–614 (2001)Google Scholar
  16. 16.
    Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  17. 17.
    Nodari, A., Ghiringhelli, M., Zamberletti, A., Albertini, S., Vanetti, M., Gallo, I.: A mobile visual search application for content based image retrieval in the fashion domain. In: Workshop on Content-Based Multimedia Indexing (2012)Google Scholar
  18. 18.
    Albertini, S., Gallo, I., Vanetti, M., Nodari, A.: Learning object segmentation using a multi network segment classification approach. In: Proceedings of International Conference on Computer Vision Theory and Applications (2012)Google Scholar
  19. 19.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes challenge (VOC) 2007-2011 resultsGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alessandro Zamberletti
    • 1
  • Ignazio Gallo
    • 1
  • Simone Albertini
    • 1
  • Marco Vanetti
    • 1
  • Angelo Nodari
    • 1
  1. 1.Dipartimento di Scienze Teoriche ed ApplicateUniversity of InsubriaVareseItaly

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