A unified approach to segmentation and categorization of dynamic textures

  • Authors:

  • Avinash Ravichandran;Paolo Favaro;René Vidal

  • Affiliations:

  • Center for Imaging Science, Johns Hopkins University, Baltimore, MD;Dept. of Electrical Engineering and Physics, Heriot-Watt University, Edinburgh, UK;Center for Imaging Science, Johns Hopkins University, Baltimore, MD

  • Venue:
  • ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
  • Year:
  • 2010

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Abstract

Dynamic textures (DT) are videos of non-rigid dynamical objects, such as fire and waves, which constantly change their shape and appearance over time. Most of the prior work on DT analysis dealt with the classification of videos of a single DT or the segmentation of videos containing multiple DTs. In this paper, we consider the problem of joint segmentation and categorization of videos of multiple DTs under varying viewpoint, scale, and illumination conditions. We formulate this problem of assigning a class label to each pixel in the video as the minimization of an energy functional composed of two terms. The first term measures the cost of assigning a DT category to each pixel. For this purpose, we introduce a bag of dynamic appearance features (BoDAF) approach, in which we fit each video with a linear dynamical system (LDS) and use features extracted from the parameters of the LDS for classification. This BoDAF approach can be applied to the whole video, thus providing a framework for classifying videos of a single DT, or to image patches (superpixels), thus providing the cost of assigning a DT category to each pixel. The second term is a spatial regularization cost that encourages nearby pixels to have the same label. The minimization of this energy functional is carried out using the random walker algorithm. Experiments on existing databases of a single DT demonstrate the superiority of our BoDAF approach with respect to state-of-the art methods. To the best of our knowledge, the problem of joint segmentation and categorization of videos of multiple DTs has not been addressed before, hence there is no standard database to test our method. We therefore introduce a new database of videos annotated at the pixel level and evaluate our approach on this database with promising results.