  Dynamic textures exist in various forms, e.g., fire, smoke, and traffic jams,
but recognizing dynamic texture is challenging due to the complex temporal
variations. In this paper, we present a novel approach stemmed from slow
feature analysis (SFA) for dynamic texture recognition. SFA extracts slowly
varying features from fast varying signals. Fortunately, SFA is capable to
leach invariant representations from dynamic textures. However, complex
temporal variations require high-level semantic representations to fully
achieve temporal slowness, and thus it is impractical to learn a high-level
representation from dynamic textures directly by SFA. In order to learn a
robust low-level feature to resolve the complexity of dynamic textures, we
propose manifold regularized SFA (MR-SFA) by exploring the neighbor
relationship of the initial state of each temporal transition and retaining the
locality of their variations. Therefore, the learned features are not only
slowly varying, but also partly predictable. MR-SFA for dynamic texture
recognition is proposed in the following steps: 1) learning feature extraction
functions as convolution filters by MR-SFA, 2) extracting local features by
convolution and pooling, and 3) employing Fisher vectors to form a video-level
representation for classification. Experimental results on dynamic texture and
dynamic scene recognition datasets validate the effectiveness of the proposed
approach.
