Abstract: A method for generating a time scheduling of a contents stream distributed to plural terminals. The contents stream is segmented in consecutive segments, each segment defining a corresponding program. The method includes: receiving auxiliary metadata including a planned time scheduling of the content stream which has been set in advance and monitoring at least a portion of a segment of the contents stream to extract corresponding content features therefrom, generating a corresponding fingerprint based on the extracted content features and assessing at least one among an actual start time and an actual end time of such segment based on a comparison between the fingerprint and a reference fingerprint associated with such segment, and updating the planned time scheduling based on the assessed at least one among the actual start time and actual end time to generate a time scheduling synchronized with the contents stream.

Abstract: A method for generating a time scheduling of a contents stream distributed to plural terminals. The contents stream is segmented in consecutive segments, each segment defining a corresponding program. The method includes: receiving auxiliary metadata including a planned time scheduling of the content stream which has been set in advance and monitoring at least a portion of a segment of the contents stream to extract corresponding content features therefrom, generating a corresponding fingerprint based on the extracted content features and assessing at least one among an actual start time and an actual end time of such segment based on a comparison between the fingerprint and a reference fingerprint associated with such segment, and updating the planned time scheduling based on the assessed at least one among the actual start time and actual end time to generate a time scheduling synchronized with the contents stream.

Abstract: A shot-based video content analysis method and system is described for providing automatic recognition of logical story units (LSUs). The method employs vector quantization (VQ) to represent the visual content of a shot, following which a shot clustering algorithm is employed together with automatic determination of merging and splitting events. The method provides an automated way of performing the time-consuming and laborious process of organising and indexing increasingly large video databases such that they can be easily browsed and searched using natural query structures.

Abstract: A shot-based video content analysis method and system is described for providing automatic recognition of logical story units (LSUs). The method employs vector quantization (VQ) to represent the visual content of a shot, following which a shot clustering algorithm is employed together with automatic determination of merging and splitting events. The method provides an automated way of performing the time-consuming and laborious process of organising and indexing increasingly large video databases such that they can be easily browsed and searched using natural query structures.