> y)). Scale-independent scalar features of each flow, based on moments of the moving point weighted by |u|, |v|,or|(u, v)|, characterize the spatial distribution of the flow. We then analyze the periodic structure of these sequences of scalars. The scalar sequences for an image sequence have the same fundamental period but differ in phase, which is a phase feature for each signal. Some phase features are consistent for one person and show significant statistical variation among persons. We use the phase feature vectors to recognize individuals by the shape of their motion. As few as three features out of the full set of twelve lead to excellent discrimination. Keywords: action recognition, gait recognition, motion features, optic flow, motion energy, spatial frequency, analysis Recognizing People by Their Gait: The Shape of Moti

Temporal textures are textures with motion. Examples include wavy water, rising steam and fire. We model image sequences of temporal textures using the spatio-temporal autoregressive model (STAR). This model expresses each pixel as a linear combination of surrounding pixels lagged both in space and in time. The model provides a base for both recognition and synthesis. We show how the least squares method can accurately estimate model parameters for large, causal neighborhoods with more than 1000 parameters. Synthesis results show that the model can adequately capture the spatial and temporal characteristics of many temporal textures. A 95% recognition rate is achieved for a 135 element database with 15 texture classes. 1.

We propose an original approach for the characterization of video dynamic content with a view to supplying new functionalities for motion-based video indexing and retrieval with query by example. We have designed a statistical framework for motion content description without any prior motion segmentation, and for motion-based video classi cation and retrieval. Contrary to other proposed methods, we do not extract from a given video sequence a set of motion features but we identify a global probabilistic model, expressed as a temporal Gibbs random eld. This leads to de ne a e cient statistical motion-based similarity measure, relying on the computation of conditional likelihoods, to discriminate various motion contents. We have carried out experiments on a set of 100 video sequences, representative of various motion situations (temporal textures as re and crowd motions, sport videos, car sequences, low motion activity examples). We have obtained promising results both for the video classi cation step and for the retrieval process. 1

The processing, description and recognition of dynamic (time-varying) textures are new exciting areas of texture analysis. Many real-world textures are dynamic textures whose retrieval from a video database should be based on both dynamic and static features. In this article, a method for extracting features revealing fundamental properties of dynamic textures is presented. These features are based on the normal flow and on the texture regularity though the sequence. Their discriminative ability is then successfully demonstrated on a full classification process.

Abstract A new issue in texture analysis is its extension to temporal domain, known as dynamic texture. Many real-world textures are dynamic textures whose retrieval from a video database should be based on both dynamic and static features. In this article, a method for extracting features revealing fundamental properties of dynamic textures is presented. Their interpretation enables qualitative requests when browsing videos. Future work is finally exposed. Keywords: Dynamic texture, video retrieval, MPEG-7, qualitative feature, normal flow. 1.