Abstract: The method comprises steps of: a) for each point of interest of each image, calculating a local gradient descriptor and a local movement descriptor; b) forming microstructures of n points of interest, each defined by a tuple of order n, with n?1; c) determining, for each tuple of a vector of structured visual characteristics (d0 . . . d3 . . . ) based on the local descriptors; d) for each tuple, mapping this vector by a classification algorithm selecting a single codeword among a set of codewords forming a codebook (CB); e) generating an ordered time series of the codewords (a0 . . . a3 . . . ) for the successive images of the video sequence; and f) measuring, by means of a function of the String Kernel type, the similarity of the time series of codewords with another time series of codewords coming from another speaker.

Abstract: The method comprises steps of: a) forming a starting set of microstructures of n points of interest, each defined by a tuple of order n, with n?1; b) determining, for each tuple, associated structured visual characteristics, based on local gradient and/or movement descriptors of the points of interest; and c) iteratively searching for and selecting the most discriminant tuples. Step c) operates by: c1) applying to the set of tuples an algorithm of the Multi-Kernel Learning MKL type; c2) extracting a sub-set of tuples producing the highest relevancy scores; c3) aggregating to these tuples an additional tuple to obtain a new set of tuples of higher order; c4) determining structured visual characteristics associated to each aggregated tuple; c5) selecting a new sub-set of most discriminant tuples; and c6) reiterating steps c1) to c5) up to a maximal order N.