Abstract
We address the problem of dynamic texture (DT) classification using optical flow features. Optical flow based approaches dominate among the currently available DT classification methods. The features used by these approaches often describe local image distortions in terms of such quantities as curl or divergence. Both normal and complete flows have been considered, with normal flow (NF) being used more frequently. However, precise meaning and applicability of normal and complete flow features have never been analysed properly. We provide a principled analysis of local image distortions and their relation to optical flow. Then we present the results of a comprehensive DT classification study that compares the performances of different flow features for a NF algorithm and four different complete flow algorithms. The efficiencies of two flow confidence measures are also studied.
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