attention-based video summarisation in rushes collection
This paper presents the framework of a general video summarisation system on the rushes collection, which formalises the summarisation process as an 0-1 Knapsack optimisation problem. Three stages are included, namely content analysis, content selection and summary composition. Content analysis is the pre-processing step, consisting of shot segmentation, feature extraction, raw video discrimination and shot clustering. Content selection weights the importance of video segments by an attention model. A greedy approximation approach is employed in the composition of summary video with the cost function, which balances the video importance gain and the duration cost. The average content coverage achieved on the rushes test collection is about 29\%, while the average qualification score on readability is 3.13 with the redundancy credit at 4.08.
video summarisation attention analysis redundancy detection