Automatic Performance Evaluation for Video Summarization.

摘要

This paper describes a system for automated performance evaluation of video summarization algorithms. We call it SUPERSIEV (System for Unsupervised Performance Evaluation of Ranked Summarization in Extended Videos). It is primarily designed for evaluating video summarization algorithms that perform frame ranking. The task of summarization is viewed as a kind of database retrieval, and we adopt some of the concepts developed for performance evaluation of retrieval in database systems. First, ground truth summaries are gathered in a user study from many assessors and for several video sequences. For each video sequence, these summaries are combined to generate a single reference file that represents the majority of assessors opinions. Then the system determines the best target reference frame for each frame of the whole video sequence and computes matching scores to form a lookup table that rates each frame. Given a summary from a candidate summarization algorithm, the system can then evaluate this summary from different aspects by computing recall, cumulated average precision, redundancy rate and average closeness. With this evaluation system, we can not only grade the quality of a video summary, but also (1) compare different automatic summarization algorithms and (2) make stepwise improvements on algorithms, without the need for new user feedback.