Synergizing spatial and temporal texture.

Abstract

Temporal texture accounts for a large proportion of motion commonly experienced in the visual world. Current temporal texture techniques extract primarily motion-based features for recognition. We propose a representation where both the spatial and the temporal aspects of texture are coupled together. Such a representation has the advantages of improving efficiency as well as retaining both spatial and temporal semantics. Flow measurements form the basis of our representation. The magnitudes and directions of the normal flow are mapped as spatiotemporal textures. These textures are then aggregated over time and are subsequently analyzed by classical texture analysis tools. Such aggregation traces the history of a motion which can be useful in the understanding of motion types. By providing a spatiotemporal analysis, our approach gains several advantages over previous implementations. The strength of our approach was demonstrated in a series of experiments, including classification and comparisons with other algorithms.