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Proceedings Paper

Adaptive anchor detection using online trained audio/visual model
Author(s): Zhu Liu; Qian Huang
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Paper Abstract

An anchor person is the hosting character in broadcast programs. Anchor segments in video often provide the landmarks for detecting the content boundaries so that it is important to identify such segments during automatic content-based multimedia indexing. Previous efforts are mostly focused on audio information or visual information alone for anchor detection using either model based methods via off-line trained models or unsupervised clustering methods. The inflexibility of the off-line model based approach and the increasing difficulty in achieving detection reliability using clustering approach lead to a new approach proposed in this paper. The goal is to detect an arbitrary anchor in a given broadcast news program. The proposed approach exploits both audio and visual cues so that on-line acoustic and visual models for the anchor can be built dynamically during data processing. In addition to the capability of identifying any given anchor, the proposed method can also be used to enhance the performance by combining with the algorithm that detects a predefined anchor. Preliminary experiment result are shown and discussed. It is demonstrated that this proposed new approach enables the flexibility of detecting an arbitrary anchor without losing the performance.

Paper Details

Date Published: 23 December 1999
PDF: 12 pages
Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999); doi: 10.1117/12.373545
Show Author Affiliations
Zhu Liu, AT&T Labs. (United States)
Qian Huang, AT&T Labs. (United States)

Published in SPIE Proceedings Vol. 3972:
Storage and Retrieval for Media Databases 2000
Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman, Editor(s)

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