Adaptive anchor detection using online trained audio/visual model

Zhu Liu; Qian Huang

doi:10.1117/12.373545

23 December 1999 Adaptive anchor detection using online trained audio/visual model

Zhu Liu, Qian Huang

Proceedings Volume 3972, Storage and Retrieval for Media Databases 2000; (1999) https://doi.org/10.1117/12.373545
Event: Electronic Imaging, 2000, San Jose, CA, United States

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.

Citation Download Citation

Zhu Liu and Qian Huang "Adaptive anchor detection using online trained audio/visual model", Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999); https://doi.org/10.1117/12.373545

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