This paper introduces a complete framework for temporal video segmentation. First, a computationally efficient shot extraction method is introduced, which adopts the normalized graph partition approach, enriched with a non-linear, multiresolution filtering of the similarity vectors involved. The shot boundary detection technique proposed yields high precision (90%) and recall (95%) rates, for all types of transitions, both abrupt and gradual. Next, for each detected shot we construct a static storyboard, by introducing a leap keyframe extraction method. The video abstraction algorithm is 23% faster than existing, state of the art techniques, for similar performances. Finally, we propose a shot grouping strategy that iteratively clusters visually similar shots, under a set of temporal constraints. Two different types of visual features are here exploited: HSV color histograms and interest points. In both cases, the precision and recall rates present average performances of 86%.