Systematic evaluation of logical story unit segmentation

Although various Logical Story Unit (LSU) segmentation methods based on visual content have been presented in literature, a common ground for comparison is missing. We present a systematic evaluation of the mutual dependencies of segmentation methods and their performances. LSUs are subjective and cannot be defined with full certainty. To limit subjectivity, we present definitions based on film theory. For evaluation, we introduce a method measuring the quality of a segmentation method and its economic impact rather than the amount of errors. Furthermore, the inherent complexity of the segmentation problem given a visual feature is measured. Also, we show to what extent LSU segmentation depends on the quality of shot boundary segmentation. To understand LSU segmentation, we present a unifying framework classifying segmentation methods into four essentially different types. We present results of an evaluation of the four types under similar circumstances using an unprecedented amount of 20 hours of 17 complete videos in different genres. Tools and ground truths are available for interactive use via internet.