A detection-based approach to broadcast news video story segmentation

A detection-based paradigm decomposes a complex system into small pieces, solves each subproblem one by one, and combines the collected evidence to obtain a final solution. In this study of video story segmentation, a set of key events are first detected from heterogeneous multimedia signal sources,...

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Bibliographic Details
Main Authors: Chengyuan Ma, Byungki Byun, Ilseo Kim, Chin-Hui Lee
Format: Conference Proceeding
Language:English
Subjects:
Broadcasting
discriminative fusion
Event detection
Fusion power generation
Image generation
Image segmentation
Large-scale systems
Multimedia communication
Multimedia systems
Ontologies
Signal generators
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Description
Summary:A detection-based paradigm decomposes a complex system into small pieces, solves each subproblem one by one, and combines the collected evidence to obtain a final solution. In this study of video story segmentation, a set of key events are first detected from heterogeneous multimedia signal sources, including a large scale concept ontology for images, text generated from automatic speech recognition systems, features extracted from audio track, and high-level video transcriptions. Then a discriminative evidence fusion scheme is investigated. We use the maximum figure-of-merit learning approach to directly optimize the performance metrics used in system evaluation, such as precision, recall, and F1 measure. Some experimental evaluations conducted on the TRECVID 2003 dataset demonstrate the effectiveness of the proposed detection-based paradigm. The proposed framework facilitates flexible combination and extensions of event detector design and evidence fusion to enable other related video applications.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2009.4959994