The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Automatic music video summarization based on audio-visual-text analysis and alignment
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Video summarisation: A conceptual framework and survey of the state of the art
Journal of Visual Communication and Image Representation
Multi-document video summarization
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
| Hi-index | 0.00 |
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.