Abstract
Among the techniques of video processing, video summarization is a promising approach to process the multimedia content. In this paper we present a novel summarization algorithm, Balanced Audio Video Maximal Marginal Relevance (Balanced AV-MMR or BAV-MMR), for multi-video summarization based on both audio and visual information. Balanced AV-MMR exploits the balance between audio information and visual information, and the balance of temporal information in different videos. Furthermore, audio genres and human face of each frame are analyzed in order to be exploited in Balanced AV-MMR. Compared with its predecessors, Video Maximal Marginal Relevance (Video-MMR) and Audio Video Maximal Marginal Relevance (AV-MMR), we design a novel mechanism to combine these indispensible features from video track and audio track and achieve better summaries.
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Li, Y., Merialdo, B. (2012). Video Summarization Based on Balanced AV-MMR. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_35
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DOI: https://doi.org/10.1007/978-3-642-27355-1_35
Publisher Name: Springer, Berlin, Heidelberg
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