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The extraction and quantization of local image and video descriptors for the subsequent creation of visual codebooks is a technique that has proved extremely effective for image and video retrieval applications. In this paper we build on this concept and extract a new set of visual descriptors that are derived from spatiotemporal salient points detected on given image sequences and provide local space-time description of the visual activity. The proposed descriptors are based on the geometrical properties of three-dimensional piecewise polynomials, namely B-splines, that are fitted on the spatiotemporal locations of the salient points that are engulfed within a given spatiotemporal neighborhood. Our descriptors are inherently translation invariant, while the use of the scales of the salient points for the definition of the neighborhood dimensions ensures space-time scaling invariance. Subsequently, a clustering algorithm is used in order to cluster our descriptors across the whole dataset and create a codebook of visual verbs, where each verb corresponds to a cluster center. We use the resulting code- book in a 'bag of verbs' approach in order to recover the pose and short-term motion of subjects at a short set of successive frames, and we use dynamic time warping (DTW) in order to align the sequences in our dataset and structure in time the recovered poses. We define a kernel based on the similarity measure provided by the DTW to classify our examples in a relevance vector machine classification scheme. We present results in a well established human activity database to verify the effectiveness of our method.