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
Hubel, D.H., Wiesel, T.N.: Brain and Visual Perception: The Story of a 25-Year Collaboration. Oxford University Press, Oxford (2005)
Lindeberg, T.: Time-causal and time-recursive spatio-temporal receptive fields. J. Math. Imaging Vis. 55, 50–88 (2016)
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)
Lindeberg, T.: A computational theory of visual receptive fields. Biol. Cybern. 107, 589–635 (2013)
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
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)
Nelson, R.C., Polana, R.: Qualitative recognition of motion using temporal texture. CVGIP: Image Underst. 56, 78–89 (1992)
Soatto, S., Doretto, G., Wu, Y.N.: Dynamic textures. In: IEEE International Conference on Computer Vision, vol. 2, pp. 439–446 (2001)
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)
Wang, L., Liu, H., Sun, F.: Dynamic texture video classification using extreme learning machine. Neurocomputing 174, 278–285 (2016)
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
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)
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)
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)
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)
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
Arashloo, S.R., Kittler, J.: Dynamic texture recognition using multiscale binarized statistical image features. IEEE Trans. Multimedia 16, 2099–2109 (2014)
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)
Schiele, B., Crowley, J.: Recognition without correspondence using multidimensional receptive field histograms. Int. J. Comput. Vis. 36, 31–50 (2000)
Xu, Y., Quan, Y., Zhang, Z., Ling, H., Ji, H.: Classifying dynamic textures via spatiotemporal fractal analysis. Pattern Recogn. 48, 3239–3248 (2015)
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)
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
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)
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)
Koenderink, J.J.: Scale-time. Biol. Cybern. 58, 159–162 (1988)
Péteri, R., Fazekas, S., Huiskes, M.J.: Dyntex: a comprehensive database of dynamic textures. Pattern Recogn. Lett. 31, 1627–1632 (2010)
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)
Jansson, Y., Lindeberg, T.: Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields (2017, in preparation)
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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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