Systems, methods, and computer program products to perform an operation comprising receiving a plurality of superclusters that includes at least one of a plurality of shot clusters, wherein each of the shot clusters includes at least one of a plurality of video shots, and wherein each video shot includes one or more video frames, and computing an expected value for a metric based on the plurality of superclusters.
BACKGROUND(1) Field of the Disclosure(2) Embodiments disclosed herein relate to computer software. More specifically, embodiments disclosed herein relate to computer software that uses the shot structure of a video as a predictor of success of the video.(3) Description of the Related Art(4) Producers of video content need analytics that can predict the success of a video before it has been released. Unlike many “predictors” of video success that are not available until after video release, a video's own raw assets are an available source of data with rich potential to predict future success of the video. Consequently, predictive models based only on video content can add to business intelligence capabilities and aid decision makers in the production and distribution of media content. However, many existing pre-release analytics are based on subjective measures that may potentially bias the results.SUMMARY(5) Embodiments disclosed herein include systems, methods, and computer program products to perform an operation comprising receiving a plurality of superclusters that includes at least one of a plurality of shot clusters, wherein each of the shot clusters includes at least one of a plurality of video shots, and wherein each video shot includes one or more video frames, and computing an expected value for a metric based on the plurality of superclusters.
1. A method, comprising: receiving a plurality of video shots of a video, wherein each video shot includes one or more frames of the video; generating a hierarchy based on the plurality of video shots, wherein the hierarchy comprises a plurality of superclusters that include at least one of a plurality of shot clusters, wherein the hierarchy describes relationships between the plurality of video shots, wherein each of the shot clusters includes at least one of the plurality of video shots; computing a value for a statistic for the video based on the hierarchy, wherein the statistic comprises a ratio of a count of the plurality of superclusters having less than four of the plurality of shot clusters to a total count of the plurality of superclusters; and computing, by operation of a computer processor, an expected value for a metric based on the statistic.
7. A computer program product, comprising: a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by a processor to perform an operation comprising: receiving a plurality of video shots of a video, wherein each video shot includes one or more frames of the video; generating a hierarchy based on the plurality of video shots, wherein the hierarchy comprises a plurality of superclusters that include at least one of a plurality of shot clusters, wherein the hierarchy describes relationships between the plurality of video shots, wherein each of the shot clusters includes at least one of the plurality of video shots; computing a value for a statistic for the video based on the hierarchy, wherein the statistic comprises a number of iterations of a clustering algorithm applied to the plurality of video shots resulting in one of the plurality of superclusters including each of the plurality of shot clusters; and computing an expected value for a metric based on the statistic.
13. A system, comprising: a processor; and a memory containing a program which when executed by the processor performs an operation comprising: receiving a plurality of video shots of a video, wherein each video shot includes one or more frames of the video; generating a hierarchy based on the plurality of video shots, wherein the hierarchy comprises a plurality of superclusters that include at least one of a plurality of shot clusters, wherein the hierarchy describes relationships between the plurality of video shots, wherein each of the shot clusters includes at least one of the plurality of video shots; computing a value for a statistic for the video based on the hierarchy, wherein the statistic comprises a ratio of a count of the plurality of superclusters having less than four of the plurality of shot clusters to a total count of the plurality of superclusters; and computing an expected value for a metric based on the statistic.