Abstract
In the last decade e-lecturing has become more and more popular. The amount of lecture video data on the World Wide Web (WWW) is growing rapidly. Therefore, a more efficient method for video retrieval in WWW or within large lecture video archives is urgently needed. This paper presents an approach for automated video indexing and video search in large lecture video archives. First of all, we apply automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. Subsequently, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames and by performing Automatic Speech Recognition (ASR) on lecture audio tracks. The OCR and ASR transcript as well as detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted respectively. Furthermore, we developed a content-based video search function and conducted a user study for evaluating the performance and the effectiveness of proposed indexing methods in our lecture video archive.KeywordsAutomatic Speech RecogText LineOptical Character RecognitionVideo IndexingVideo SearchThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
