Rushes video summarization using audio-visual information and sequence alignment
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
International Journal of Web and Grid Services
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International Journal of Business Information Systems
EURASIP Journal on Advances in Signal Processing
Digital learning video indexing using scene detection
ICHL'11 Proceedings of the 4th international conference on Hybrid learning
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In instructional videos of chalk board presentations, the visual content refers to the text and figures written on the boards. Existing methods on video summarization are not effective for this video domain because they are mainly based on low-level image features such as color and edges. In this work, we present a novel approach to summarizing the visual content in instructional videos using middle-level features. We first develop a robust algorithm to extract content text and figures from instructional videos by statistical modelling and clustering. This algorithm addresses the image noise, nonuniformity of the board regions, camera movements, occlusions, and other challenges in the instructional videos that are recorded in real classrooms. Using the extracted text and figures as the middle level features, we retrieve a set of key frames that contain most of the visual content. We further reduce content redundancy and build a mosaicked summary image by matching extracted content based on K-th Hausdorff distance and connected component decomposition. Performance evaluation on four full-length instructional videos shows that our algorithm is highly effective in summarizing instructional video content.