Visual Summarization of Lecture Video Segments for Enhanced Navigation

by   Mohammad Rajiur Rahman, et al.

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 frequently selected images as the ground truth, and 94 F1-measure with the union of all user selected images as the ground truth. For 98 selected that image for their summary or considered it similar to another image they selected. Over 65 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.


page 2

page 3

page 6


How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization

Automatic video summarization is still an unsolved problem due to severa...

TL;DW? Summarizing Instructional Videos with Task Relevance Cross-Modal Saliency

YouTube users looking for instructions for a specific task may spend a l...

Real-time Video Summarization on Commodity Hardware

We present a method for creating video summaries in real-time on commodi...

Scaling New Peaks: A Viewership-centric Approach to Automated Content Curation

Summarizing video content is important for video streaming services to e...

A Framework towards Domain Specific Video Summarization

In the light of exponentially increasing video content, video summarizat...

A Closer Look at Temporal Ordering in the Segmentation of Instructional Videos

Understanding the steps required to perform a task is an important skill...

SafeVchat: Detecting Obscene Content and Misbehaving Users in Online Video Chat Services

Online video chat services such as Chatroulette, Omegle, and vChatter th...

Please sign up or login with your details

Forgot password? Click here to reset