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Dynamic Texture Recognition Using Time-Causal Spatio-Temporal Scale-Space Filters

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Book cover Scale Space and Variational Methods in Computer Vision (SSVM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10302))

Abstract

This work presents an evaluation of using time-causal scale-space filters as primitives for video analysis. For this purpose, we present a new family of video descriptors based on regional statistics of spatio-temporal scale-space filter responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain. We evaluate one member in this family, constituting a joint binary histogram, on two widely used dynamic texture databases. The experimental evaluation shows competitive performance compared to previous methods for dynamic texture recognition, especially on the more complex DynTex database. These results support the descriptive power of time-causal spatio-temporal scale-space filters as primitives for video analysis.

The support from the Swedish Research Council (Contract 2014-4083) and Stiftelsen Olle Engkvist Byggmästare (Contract 2015/465) is gratefully acknowledged.

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References

  1. Hubel, D.H., Wiesel, T.N.: Brain and Visual Perception: The Story of a 25-Year Collaboration. Oxford University Press, Oxford (2005)

    Google Scholar 

  2. Lindeberg, T.: Time-causal and time-recursive spatio-temporal receptive fields. J. Math. Imaging Vis. 55, 50–88 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  3. Lindeberg, T.: Generalized Gaussian scale-space axiomatics comprising linear scale-space, affine scale-space and spatio-temporal scale-space. J. Math. Imaging Vis. 40, 36–81 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  4. Lindeberg, T.: A computational theory of visual receptive fields. Biol. Cybern. 107, 589–635 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chetverikov, D., Péteri, R.: A brief survey of dynamic texture description and recognition. In: Kurzyński, M., Puchała, E., Woźniak, M., Żołnierek, A. (eds.) Computer Recognition Systems. AINSC, vol. 30, pp. 17–26. Springer, Heidelberg (2005). doi:10.1007/3-540-32390-2_2

    Chapter  Google Scholar 

  6. Linde, O., Lindeberg, T.: Composed complex-cue histograms: an investigation of the information content in receptive field based image descriptors for object recognition. Comput. Vis. Image Underst. 116, 538–560 (2012)

    Article  Google Scholar 

  7. Nelson, R.C., Polana, R.: Qualitative recognition of motion using temporal texture. CVGIP: Image Underst. 56, 78–89 (1992)

    Article  MATH  Google Scholar 

  8. Soatto, S., Doretto, G., Wu, Y.N.: Dynamic textures. In: IEEE International Conference on Computer Vision, vol. 2, pp. 439–446 (2001)

    Google Scholar 

  9. Ravichandran, A., Chaudhry, R., Vidal, R.: View-invariant dynamic texture recognition using a bag of dynamical systems. In: Computer Vision and Pattern Recognition, pp. 1651–1657 (2009)

    Google Scholar 

  10. Wang, L., Liu, H., Sun, F.: Dynamic texture video classification using extreme learning machine. Neurocomputing 174, 278–285 (2016)

    Article  Google Scholar 

  11. Wildes, R.P., Bergen, J.R.: Qualitative spatiotemporal analysis using an oriented energy representation. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 768–784. Springer, Heidelberg (2000). doi:10.1007/3-540-45053-X_49

    Chapter  Google Scholar 

  12. Derpanis, K.G., Wildes, R.P.: Spacetime texture representation and recognition based on a spatiotemporal orientation analysis. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1193–1205 (2012)

    Article  Google Scholar 

  13. Gonçalves, W.N., Machado, B.B., Bruno, O.M.: Spatiotemporal Gabor filters: a new method for dynamic texture recognition. arXiv preprint arXiv:1201.3612 (2012)

  14. Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29, 915–928 (2007)

    Article  Google Scholar 

  15. Ren, J., Jiang, X., Yuan, J.: Dynamic texture recognition using enhanced LBP features. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2400–2404 (2013)

    Google Scholar 

  16. Hong, S., Ryu, J., Yang, H.S.: Not all frames are equal: aggregating salient features for dynamic texture classification. In: Multidimensional Systems and Signal Processing (2016). doi:10.1007/s11045-016-0463-7

  17. Arashloo, S.R., Kittler, J.: Dynamic texture recognition using multiscale binarized statistical image features. IEEE Trans. Multimedia 16, 2099–2109 (2014)

    Article  Google Scholar 

  18. Quan, Y., Huang, Y., Ji, H.: Dynamic texture recognition via orthogonal tensor dictionary learning. In: IEEE International Conference on Computer Vision, pp. 73–81 (2015)

    Google Scholar 

  19. Schiele, B., Crowley, J.: Recognition without correspondence using multidimensional receptive field histograms. Int. J. Comput. Vis. 36, 31–50 (2000)

    Article  Google Scholar 

  20. Xu, Y., Quan, Y., Zhang, Z., Ling, H., Ji, H.: Classifying dynamic textures via spatiotemporal fractal analysis. Pattern Recogn. 48, 3239–3248 (2015)

    Article  Google Scholar 

  21. Ji, H., Yang, X., Ling, H., Xu, Y.: Wavelet domain multifractal analysis for static and dynamic texture classification. IEEE Trans. Image Process. 22, 286–299 (2013)

    Article  MathSciNet  Google Scholar 

  22. Ghanem, B., Ahuja, N.: Maximum margin distance learning for dynamic texture recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 223–236. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15552-9_17

    Chapter  Google Scholar 

  23. Yang, F., Xia, G.S., Liu, G., Zhang, L., Huang, X.: Dynamic texture recognition by aggregating spatial and temporal features via ensemble SVMs. Neurocomputing 173, 1310–1321 (2016)

    Article  Google Scholar 

  24. Qi, X., Li, C., Guoying, Z., Hong, X., Pietikäinen, M.: Dynamic texture and scene classification by transferring deep image features. Neurocomputing 171, 1230–1241 (2016)

    Article  Google Scholar 

  25. Koenderink, J.J.: Scale-time. Biol. Cybern. 58, 159–162 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  26. Péteri, R., Fazekas, S., Huiskes, M.J.: Dyntex: a comprehensive database of dynamic textures. Pattern Recogn. Lett. 31, 1627–1632 (2010)

    Article  Google Scholar 

  27. Dubois, S., Péteri, R., Ménard, M.: Characterization and recognition of dynamic textures based on the 2D+T curvelet transform. Sig. Image Video Process. 9, 819–830 (2015)

    Article  Google Scholar 

  28. Jansson, Y., Lindeberg, T.: Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields (2017, in preparation)

    Google Scholar 

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Authors and Affiliations

  1. Computational Brain Science Lab, Department of Computational Science and Technology, School of Computer Science and Communication, KTH Royal Institute of Technology, Stockholm, Sweden

    Ylva Jansson & Tony Lindeberg

Authors
  1. Ylva Jansson
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  2. Tony Lindeberg
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Correspondence to Ylva Jansson .

Editor information

Editors and Affiliations

  1. University of Copenhagen , Copenhagen, Denmark

    François Lauze

  2. Technical University of Denmark , Kongens Lyngby, Denmark

    Yiqiu Dong

  3. Technical University of Denmark , Kongens Lyngby, Denmark

    Anders Bjorholm Dahl

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Jansson, Y., Lindeberg, T. (2017). Dynamic Texture Recognition Using Time-Causal Spatio-Temporal Scale-Space Filters. In: Lauze, F., Dong, Y., Dahl, A. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2017. Lecture Notes in Computer Science(), vol 10302. Springer, Cham. https://doi.org/10.1007/978-3-319-58771-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-58771-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58770-7

  • Online ISBN: 978-3-319-58771-4

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