Spatiotemporal lacunarity spectrum for dynamic texture classification

Dynamic texture (DT) in videos is the combination of texture patterns with motion pat-terns, and DT recognition is a key step in many vision-related applications. Owing to the additional challenges arising from the characterization on temporal organizations of texture elements, the recognition on DTs is more dif?cult than that on static textures. In this paper, a DT descriptor for classi?cation is constructed, which examines the stationary irregularities of spatial and temporal distributions of local binary patterns in DT slices and encodes the irregularities by lacunarity-based features. The proposed descriptor has strong robustness to monotonic illumination changes and modest viewpoint changes, as well as strong discriminability in classification. In comparison with histogram-based methods, our approach is capable of encoding spatio-temporal details on the distribution of DT patterns. It also encodes additional details on the layout of DT patterns that recent fractal-based methods ignore. The proposed descriptor was applied to DT classification, and the experimental results show its power on several benchmark datasets.