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International Journal of Computer Vision

, Volume 51, Issue 2, pp 91–109 | Cite as

Dynamic Textures

  • Gianfranco Doretto
  • Alessandro Chiuso
  • Ying Nian Wu
  • Stefano Soatto
Article

Abstract

Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity properties in time; these include sea-waves, smoke, foliage, whirlwind etc. We present a characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing. We borrow tools from system identification to capture the “essence” of dynamic textures; we do so by learning (i.e. identifying) models that are optimal in the sense of maximum likelihood or minimum prediction error variance. For the special case of second-order stationary processes, we identify the model sub-optimally in closed-form. Once learned, a model has predictive power and can be used for extrapolating synthetic sequences to infinite length with negligible computational cost. We present experimental evidence that, within our framework, even low-dimensional models can capture very complex visual phenomena.

textures dynamic scene analysis 3D textures minimum description length image compression generative model prediction error methods ARMA model subspace system identification canonical correlation learning 

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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Gianfranco Doretto
    • 1
  • Alessandro Chiuso
    • 2
  • Ying Nian Wu
    • 3
  • Stefano Soatto
    • 1
  1. 1.Computer Science DepartmentUniversity of CaliforniaLos Angeles
  2. 2.Dipartimento di Ingegneria dell'InformazioneUniversità di PadovaItaly
  3. 3.Statistics DepartmentUniversity of CaliforniaLos Angeles

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