Non-parametric motion activity analysis for statistical retrieval with partial query

We present an original approach for motion-based video retrieval involving partial query. More precisely, we propose a unified statistical framework allowing us to simultaneously extract entities of interest in video shots and supply the associated content-based characterization, which can be used to satisfy partial queries. It relies on the analysis of motion activity in video sequences based on a non-parametric probabilistic modeling of motion information. Areas comprising relevant types of motion activity are extracted from a Markovian region-level labeling applied to the adjacency graph of an initial block-based partition of the image. As a consequence, given a set of videos, we are able to construct a structured base of samples of entities of interest represented by their associated statistical models of motion activity. The retrieval operations is then formulated as a Bayesian inference issue using the MAP criterion. We report different results of extraction of entities of interest in video sequences and examples of retrieval operations performed on a base composed of one hundred video samples.