Structuring low-quality videotaped lectures for cross-reference browsing by video text analysis

• Main Authors: WANG, Feng, NGO, Chong-wah, PONG, Ting-Chuen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2008
• Subjects: topic detection; video text analysis; super-resolution reconstruction; synchronization of lecture videos and electronic slides; Computer Sciences; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/6345; https://ink.library.smu.edu.sg/context/sis_research/article/7348/viewcontent/Structuring_low_quality_videotaped_lectures_for_cross_reference_browsing_by_video_text_analysis.pdf
• Institution: Singapore Management University

This paper presents an unified approach in analyzing and Structuring the content of videotaped lectures for distance learning applications. By Structuring lecture videos, we can Support topic indexing and semantic querying of multimedia documents captured in the traditional classrooms. Our goal in this paper is to automatically construct the cross references of lecture videos and textual documents so as to facilitate the synchronized browsing and presentation of multimedia information. The major issues involved in our approach are topical event detection, video text analysis and the matching of slide shots and external documents. In topical event detection, a novel transition detector is proposed to rapidly locate the slide shot boundaries by computing the changes of text and background regions in videos. For each detected topical event, multiple keyframes are extracted for video text detection, super-resolution reconstruction, binarization and recognition. A new approach for the reconstruction of high-resolution textboxes based on linear interpolation and multi-frame integration is also proposed for the effective binarization and recognition. The recognized characters are utilized to match the video slide shots and external documents based on our proposed title and content similarity measures. (C) 2008 Elsevier Ltd. All rights reserved.