The next step was to use those important frames, through the video, and look for early ones that influence later ones. To do that they adapted a method developed by researchers at Carnegie Mellon University that could predict how one news article leads to another, assembling a series of articles to transition from a starting point to a known end point.
For the text work, researchers used word frequencies and correlations across articles to quantify influence. For the video work, Grauman and Lu used their significant objects and frames to do the same. Then they were able to identify a chain of video clips that efficiently filled in the story from beginning to end."We ran human 'taste tests' comparing our method to previous methods," said Grauman, "and between 75 and 90 percent of people evaluating the summaries, depending on the datasets and method being compared, found that our system is superior."
Grauman said that as video summarization techniques continue to improve, they will become invaluable aids not just to people with very specialized needs, like police investigators and those suffering from memory loss, but to everyday Web surfers as well.
"My hope is that we'll be able to get video browsing much closer to what we experience with image browsing," she said. "Consider browsing 50 images on a webpage. It's manageable, since you can scroll down and see them all in one pass. Now imagine trying to browse 50 videos online. It's simply not efficient. We need summarization algorithms in order to improve video search considerably."