Unsupervised news video segmentation by combined audio-video analysis

Authors:
M. De Santo;G. Percannella;C. Sansone;M. Vento
Affiliations:
Dip. di Ingegneria dell'Informazione ed Ingegneria Elettrica, Università degli Studi di Salerno, Fisciano (SA), Italy;Dip. di Ingegneria dell'Informazione ed Ingegneria Elettrica, Università degli Studi di Salerno, Fisciano (SA), Italy;Dipartimento di Informatica e Sistemistica, Università degli Studi di Napoli “Federico II”, Napoli, Italy;Dip. di Ingegneria dell'Informazione ed Ingegneria Elettrica, Università degli Studi di Salerno, Fisciano (SA), Italy
Venue:
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Year:
2006

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Abstract

Segmenting news video into stories is among key issues for achieving efficient treatment of news-based digital libraries. In this paper we present a novel unsupervised algorithm that combines audio and video information for automatic partitioning news videos into stories. The proposed algorithm is based on the detection of anchor shots within the video. In particular, a set of audio/video templates of anchorperson shots is first extracted in an unsupervised way, then shots are classified by comparing them to the templates using both video and audio similarity. Finally, a story is obtained by linking each anchor shot with all successive shots until another anchor shot, or the end of the news video, occurs. Audio similarity is evaluated by means of a new index and helps to achieve better performance in anchor shot detection than pure video approach. The method has been tested on a wide database and compared with other state-of-the-art algorithms, demonstrating its effectiveness with respect to them.