Video summarization based on balanced AV-MMR

Li, Yingbo; Mérialdo, Bernard

MMM 2012, 18th International Conference on Multimedia Modeling, 4-6 January, 2012, Klagenfurt, Austria / Also published in Springer LNCS, Vol 7131/2012

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 AVMMR 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.

Detail

Document

DOI

BIBTEX

Type:

Conférence

City:

Klagenfurt

Date:

2012-01-04

Department:

Data Science

Eurecom Ref:

3510

© Springer. Personal use of this material is permitted. The definitive version of this paper was published in MMM 2012, 18th International Conference on Multimedia Modeling, 4-6 January, 2012, Klagenfurt, Austria / Also published in Springer LNCS, Vol 7131/2012 and is available at : http://dx.doi.org/10.1007/978-3-642-27355-1_35