Color-Aware Local Spatiotemporal Features for Action Recognition

  • Fillipe Souza
  • Eduardo Valle
  • Guillermo Chávez
  • Arnaldo de A. Araújo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7042)


Despite the recent developments in spatiotemporal local features for action recognition in video sequences, local color information has so far been ignored. However, color has been proved an important element to the success of automated recognition of objects and scenes. In this paper we extend the space-time interest point descriptor STIP to take into account the color information on the features’ neighborhood. We compare the performance of our color-aware version of STIP (which we have called HueSTIP) with the original one.


Color invariance spatiotemporal local features human action recognition 


  1. 1.
    Gevers, T., Stokman, H.: Robust histogram construction from color invariants for object recognition. PAMI 26, 113–117 (2004)CrossRefGoogle Scholar
  2. 2.
    van de Weijer, J., Schmid, C.: Coloring local feature extraction. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part II. LNCS, vol. 3952, pp. 334–348. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. PAMI 32(9), 1582–1596 (2010)CrossRefGoogle Scholar
  4. 4.
    Laptev, I.: On space-time interest points. IJCV 64(2-3), 107–123 (2005)CrossRefGoogle Scholar
  5. 5.
    Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. IJCV 60(1), 63–86 (2004)CrossRefGoogle Scholar
  6. 6.
    Wang, H., Ullah, M.M., Kläser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: BMVC, p. 127 (September 2009)Google Scholar
  7. 7.
    Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: VS-PETS (October 2005)Google Scholar
  8. 8.
    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. 5303, pp. 650–663. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Marszałek, M., Laptev, I., Schmid, C.: Actions in context. In: CVPR (2009)Google Scholar
  11. 11.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: 4th Alvey Vision Conf., pp. 147–152 (1988)Google Scholar
  12. 12.
    Forstner, W., Gulch, E.: A fast operator for detection and precise location of distinct points, corners and centres of circular features. In: ISPRS, pp. 281–305 (1987)Google Scholar
  13. 13.
    Laptev, I., Marszałek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (June 2008)Google Scholar
  14. 14.
    Viitaniemi, V., Laaksonen, J.: Experiments on selection of codebooks for local image feature histograms. In: Sebillo, M., Vitiello, G., Schaefer, G. (eds.) VISUAL 2008. LNCS, vol. 5188, pp. 126–137. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), software

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fillipe Souza
    • 1
  • Eduardo Valle
    • 2
  • Guillermo Chávez
    • 3
  • Arnaldo de A. Araújo
    • 1
  1. 1.NPDI LabDCC/UFMGBelo HorizonteBrazil
  2. 2.RECOD LabIC/UnicampCampinasBrazil
  3. 3.ICEB/UFOPOuro PretoBrazil

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