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. © 2012 Springer-Verlag.
Author supplied keywords
Cite
CITATION STYLE
Li, Y., & Merialdo, B. (2012). Video summarization based on balanced AV-MMR. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7131 LNCS, pp. 370–382). https://doi.org/10.1007/978-3-642-27355-1_35
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
