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Space-Time Zernike Moments and Pyramid Kernel Descriptors for Action Classification

  • Luca Costantini
  • Lorenzo Seidenari
  • Giuseppe Serra
  • Licia Capodiferro
  • Alberto Del Bimbo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6979)

Abstract

Action recognition in videos is a relevant and challenging task of automatic semantic video analysis. Most successful approaches exploit local space-time descriptors. These descriptors are usually carefully engineered in order to obtain feature invariance to photometric and geometric variations. The main drawback of space-time descriptors is high dimensionality and efficiency. In this paper we propose a novel descriptor based on 3D Zernike moments computed for space-time patches. Moments are by construction not redundant and therefore optimal for compactness. Given the hierarchical structure of our descriptor we propose a novel similarity procedure that exploits this structure comparing features as pyramids. The approach is tested on a public dataset and compared with state-of-the art descriptors.

Keywords

video annotation action classification Zernike moments 

References

  1. 1.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  2. 2.
    Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: Proc. of ICCV (2003)Google Scholar
  3. 3.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Van Gool, L.: A comparison of affine region detectors. International Journal of Computer Vision 65(1-2) (2005)Google Scholar
  4. 4.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(10) (2005)Google Scholar
  5. 5.
    Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: Proc. of CVPR (2008)Google Scholar
  6. 6.
    Willems, G., Tuytelaars, T., Van Gool, L.: An efficient dense and scale-invariant spatio-temporal interest point detector. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. ECCV, pp. 650–663. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition. In: Proc. of ACM Multimedia (2007)Google Scholar
  8. 8.
    Kläser, A., Marszałek, M., Schmid, C.: A spatio-temporal descriptor based on 3d-gradients. In: Proc. of BMVC (2008)Google Scholar
  9. 9.
    Ballan, L., Bertini, M., Del Bimbo, A., Seidenari, L., Serra, G.: Recognizing human actions by fusing spatio-temporal appearance and motion descriptors. In: Proc. of ICIP (2009)Google Scholar
  10. 10.
    Flusser, J., Zitova, B., Suk, T.: Moments and Moment Invariants in Pattern Recognition. Wiley Publishing, Chichester (2009)CrossRefzbMATHGoogle Scholar
  11. 11.
    Li, S., Lee, M.C., Pun, C.M.: Complex zernike moments features for shape-based image retrieval. IEEE Transactions on Systems, Man, and Cybernetics (2009)Google Scholar
  12. 12.
    Sun, X., Chen, M., Hauptmann, A.: Action recognition via local descriptors and holistic features. In: Proc. of Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB) (2009)Google Scholar
  13. 13.
    Bo, L., Ren, X., Fox, D.: Kernel descriptors for visual recognition. In: Advances in Neural Information Processing Systems (2010)Google Scholar
  14. 14.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proc. of CVPR (2006)Google Scholar
  15. 15.
    Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: Proc. of ICCV (2005)Google Scholar
  16. 16.
    Neri, A., Carli, M., Palma, V., Costantini, L.: Image search based on quadtree zernike decomposition. Journal of Electronic Imaging 19(4) (2010)Google Scholar
  17. 17.
    Li, S., Lee, M.C., Pun, C.M.: Complex zernike moments features for shape-based image retrieval. IEEE Transactions on Systems, Man, and Cybernetics 39(1) (2009)Google Scholar
  18. 18.
    Canterakis, N.: 3d zernike moments and zernike affine invariants for 3d image analysis and recognition. In: Proc. of Conference on Image Analysis (1999)Google Scholar
  19. 19.
    Novotni, M., Klein, R.: Shape retrieval using 3d zernike descriptors. Computer-Aided Design 36(11), 1047–1062 (2004)CrossRefGoogle Scholar
  20. 20.
    Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: Proc. of VSPETS (2005)Google Scholar
  21. 21.
    Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: Proc of. CVPR (2008)Google Scholar
  22. 22.
    Mattivi, R., Shao, L.: Human action recognition using LBP-TOP as sparse spatio-temporal feature descriptor. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 740–747. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luca Costantini
    • 2
  • Lorenzo Seidenari
    • 1
  • Giuseppe Serra
    • 1
  • Licia Capodiferro
    • 2
  • Alberto Del Bimbo
    • 1
  1. 1.Media Integration and Communication CenterUniversity of FlorenceItaly
  2. 2.Fondazione Ugo BordoniRomaItaly

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