Analysis and Interface for Instructional Video

TL;DR

This paper introduces a new segmentation method and an innovative user interface for indexing and visualizing the semantic content of instructional videos, enhancing user navigation and content understanding.

Contribution

It presents a novel segmentation algorithm with high accuracy and a user interface based on topologically linked icons, validated through user studies and analysis of extensive instructional videos.

Findings

Segmentation accuracy exceeds 96% on 17 instructional videos.

The user interface effectively displays related topics for quick navigation.

The clustering algorithm operates with near-linear computational cost.

Abstract

We present a new method for segmenting, and a new user interface for indexing and visualizing, the semantic content of extended instructional videos. Using various visual filters, key frames are first assigned a media type (board, class, computer, illustration, podium, and sheet). Key frames of media type board and sheet are then clustered based on contents via an algorithm with near-linear cost. A novel user interface, the result of two user studies, displays related topics using icons linked topologically, allowing users to quickly locate semantically related portions of the video. We analyze the accuracy of the segmentation tool on 17 instructional videos, each of which is from 75 to 150 minutes in duration (a total of 40 hours); it exceeds 96%.