Learn More
Most time-series clustering methods, such as k-means or k-medoids, are initialized by prior knowledge about the number of classes or by a learning step. We propose an unsupervised clustering technique based on spatiotemporal mean-shift and optimal time series warping using dynamic time warping (DTW). Our main contribution consists in combining a(More)
In this paper, we propose a mean-shift formulation allowing spatiotemporal clustering of video streams, and possibly extensible to other multivariate evolving data. Our formulation enables causal or omniscient filtering of spatiotemporal data, which is robust to total object occlusions. It embeds a new clustering algorithm within the filtering procedure(More)
In this paper, we propose a new anisotropic diffusion formulation allowing non-linear spatiotemporal filtering of image sequences. We first formulate a multidimensional spatiotemporal diffusion equation based on Barash's iterative form, processing independently both spatial, temporal and intensity dimensions with their own diffusion functions and scale(More)
  • 1