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

  • Ylva JanssonEmail author
  • Tony Lindeberg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Receptive Field Local Binary Pattern Support Vector Machine Classifier Near Neighbour Linear Dynamical System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computational Brain Science Lab, Department of Computational Science and Technology, School of Computer Science and CommunicationKTH Royal Institute of TechnologyStockholmSweden

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