Lecture Video Segmentation

Abstract

In multimedia-based e-learning systems, the accessibility and searchability of most lecture video content is still insufficient due to the unscripted and spontaneous speech of the speakers. Thus, it is very desirable to enable people to navigate and access specific topics within lecture videos by performing an automatic topic-wise video segmentation. This problem becomes even more challenging when the quality of such lecture videos is not sufficiently high. To this end, we first present the ATLAS system that has two main novelties: (i) a SVM hmm model is proposed to learn temporal transition cues and (ii) a fusion scheme is suggested to combine transition cues extracted from heterogeneous information of lecture videos. Subsequently, considering that contextual information is very useful in determining knowledge structures, we present the TRACE system to automatically perform such a segmentation based on a linguistic approach using Wikipedia texts. TRACE has two main contributions: (i) the extraction of a novel linguistic-based Wikipedia feature to segment lecture videos efficiently, and (ii) the investigation of the late fusion of video segmentation results derived from state-of-the-art algorithms. Specifically for the late fusion, we combine confidence scores produced by the models constructed from visual, transcriptional, and Wikipedia features. According to our experiments on lecture videos from VideoLectures. NET and NPTEL, the proposed algorithms in the ATLAS and TRACE systems segment knowledge structures more accurately compared to existing state-of-the-art algorithms.