  Lecture videos are an increasingly important learning resource for higher
education. However, the challenge of quickly finding the content of interest in
a lecture video is an important limitation of this format. This paper
introduces visual summarization of lecture video segments to enhance
navigation. A lecture video is divided into segments based on the
frame-to-frame similarity of content. The user navigates the lecture video
content by viewing a single frame visual and textual summary of each segment.
The paper presents a novel methodology to generate the visual summary of a
lecture video segment by computing similarities between images extracted from
the segment and employing a graph-based algorithm to identify the subset of
most representative images. The results from this research are integrated into
a real-world lecture video management portal called Videopoints. To collect
ground truth for evaluation, a survey was conducted where multiple users
manually provided visual summaries for 40 lecture video segments. The users
also stated whether any images were not selected for the summary because they
were similar to other selected images. The graph based algorithm for
identifying summary images achieves 78% precision and 72% F1-measure with
frequently selected images as the ground truth, and 94% precision and 72%
F1-measure with the union of all user selected images as the ground truth. For
98% of algorithm selected visual summary images, at least one user also
selected that image for their summary or considered it similar to another image
they selected. Over 65% of automatically generated summaries were rated as good
or very good by the users on a 4-point scale from poor to very good. Overall,
the results establish that the methodology introduced in this paper produces
good quality visual summaries that are practically useful for lecture video
navigation.
