VideoSET: Video Summary Evaluation through Text
Table 1: Spearman correlation coefficients of human judgment with automated evaluation of video summaries generated using existing video summarization methods.
['[EMPTY]', 'Daily life dataset ', 'Disney dataset ', 'TV episodes']
We correlated rankings based on the VideoSET and pixel-based scores with human rankings from a user study. Humans were asked to rank the video summaries generated using the above methods, in terms of how semantically similar they were to the content of ground-truth written summaries. The score was taken to be the highest score with respect to 3 ground-truth summaries. The largest correlation gap between VideoSET and the pixel-based distance is for the Disney dataset, which is most challenging due to the highly varied visual scenes as the subjects tour through the amusement park. The smallest correlation gap is for the TV episodes, where both methods perform strongly due to the fact that TV shows are highly edited with little redundancy.