TY - JOUR
T1 - A system for the semantic multimodal analysis of news audio-visual content
AU - Mezaris, Vasileios
AU - Gidaros, Spyros
AU - Papadopoulos, Georgios Th.
AU - Kasper, Walter
AU - Ordelman, Roeland J.F.
AU - Steffen, Jörg
AU - Huijbregts, M.A.H.
AU - de Jong, Franciska M.G.
AU - Kompatsiaris, Ioannis
AU - Strintzis, Michael G.
N1 - 10.1155/2010/645052
PY - 2010/2
Y1 - 2010/2
N2 - News-related content is nowadays among the most popular types of content for users in everyday applications. Although the generation and distribution of news content has become commonplace, due to the availability of inexpensive media capturing devices and the development of media sharing services targeting both professional and user-generated news content, the automatic analysis and annotation that is required for supporting intelligent search and delivery of this content remains an open issue. In this paper, a complete architecture for knowledge-assisted multimodal analysis of news-related multimedia content is presented, along with its constituent components. The proposed analysis architecture employs state-of-the-art methods for the analysis of each individual modality (visual, audio, text) separately and proposes a novel fusion technique based on the particular characteristics of news-related content for the combination of the individual modality analysis results. Experimental results on news broadcast video illustrate the usefulness of the proposed techniques in the automatic generation of semantic annotations.
AB - News-related content is nowadays among the most popular types of content for users in everyday applications. Although the generation and distribution of news content has become commonplace, due to the availability of inexpensive media capturing devices and the development of media sharing services targeting both professional and user-generated news content, the automatic analysis and annotation that is required for supporting intelligent search and delivery of this content remains an open issue. In this paper, a complete architecture for knowledge-assisted multimodal analysis of news-related multimedia content is presented, along with its constituent components. The proposed analysis architecture employs state-of-the-art methods for the analysis of each individual modality (visual, audio, text) separately and proposes a novel fusion technique based on the particular characteristics of news-related content for the combination of the individual modality analysis results. Experimental results on news broadcast video illustrate the usefulness of the proposed techniques in the automatic generation of semantic annotations.
KW - EWI-19888
KW - IR-76505
KW - METIS-276406
U2 - 10.1155/2010/645052
DO - 10.1155/2010/645052
M3 - Article
VL - 2010
SP - 645052
JO - EURASIP journal on advances in signal processing
JF - EURASIP journal on advances in signal processing
SN - 1687-6172
IS - 47
ER -