An Accurate Lecture Video Segmentation Method by Using SIFT and Adaptive Threshold

Abstract

Much research has been done in the past for segmenting lecture videos by detecting slide transitions. However, they do not perform well on certain kinds of videos recorded under non-stationary settings: the changes of a camera position or focus during a lecture. Since such non-stationary settings greatly affect visual properties of slides, the existing approaches utilizing global features and a global threshold, often have trouble in computing similarities between slides. In this paper, we propose a highly accurate method for lecture video segmentation by using SIFT and an adaptive threshold. By using SIFT, we can reliably match two slides whose contents are the same but are visually different. We also propose an adaptive threshold selection algorithm that detects slide transitions accurately by considering characteristics of features. Through various experiments that use real lecture videos, we show that our method provides 30% improvement in the average F1-score over other existing methods.