Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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. Moreover, this problem becomes even more challenging when the quality of such lecture videos is not sufficiently high. To extract the structural knowledge of a multi-topic lecture video and thus make it easily accessible it is very desirable to divide each video into shorter clips by performing an automatic topic-wise video segmentation. To this end, this paper presents 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,  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 algorithm segments knowledge structures more accurately compared to existing state-of-the-art algorithms. The evaluation results are very encouraging and thus confirm the effectiveness of TRACE.
['Rajiv Ratn Shah', 'Yi Yu', 'Anwar Dilawar Shaikh', 'Roger Zimmermann']
I am writing a research paper on a idea TRACE: Linguistic-Based Approach for Automatic Lecture Video Segmentation Leveraging Wikipedia Texts
[{"from": "human", "value": "I am writing a research paper on a idea TRACE: Linguistic-Based Approach for Automatic Lecture Video Segmentation Leveraging Wikipedia Texts"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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. Moreover, this problem becomes even more challenging when the quality of such lecture videos is not sufficiently high. To extract the structural knowledge of a multi-topic lecture video and thus make it easily accessible it is very desirable to divide each video into shorter clips by performing an automatic topic-wise video segmentation. To this end, this paper presents 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,  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 algorithm segments knowledge structures more accurately compared to existing state-of-the-art algorithms. The evaluation results are very encouraging and thus confirm the effectiveness of TRACE."}]