Characterizing dynamic textures with space-time lacunarity analysis

This paper addresses the challenge of reliably capturing the temporal characteristics of local space-time patterns in dynamic texture (DT). A powerful DT descriptor is proposed, which enjoys strong robustness to viewpoint changes, illumination changes, and video deformation. Observing that local DT patterns are spatial-temporally distributed with stationary irregularities, we proposed to characterize the distributions of local binarized DT patterns along both the temporal and the spatial axes via lacunarity analysis. We also observed such irregularities are similar on the DT slices along the same axis but distinct between axes. Thus, the resulting lacunarity based features are averaged along each axis and concatenated as the final DT descriptor. We applied the proposed DT descriptor to DT classification and evaluated its performance on several benchmark datasets. The experimental results have demonstrated the power of the proposed descriptor in comparison with existing ones.