Title
Multimodal concept detection in broadcast media: KavTan
Abstract
Concept detection stands as an important problem for efficient indexing and retrieval in large video archives. In this work, the KavTan System, which performs high-level semantic classification in one of the largest TV archives of Turkey, is presented. In this system, concept detection is performed using generalized visual and audio concept detection modules that are supported by video text detection, audio keyword spotting and specialized audio-visual semantic detection components. The performance of the presented framework was assessed objectively over a wide range of semantic concepts (5 high-level, 14 visual, 9 audio, 2 supplementary) by using a significant amount of precisely labeled ground truth data. KavTan System achieves successful high-level concept detection performance in unconstrained TV broadcast by efficiently utilizing multimodal information that is systematically extracted from both spatial and temporal extent of multimedia data.
Year
DOI
Venue
2014
10.1007/s11042-013-1564-z
Multimedia Tools Appl.
Keywords
Field
DocType
broadcast video indexing,concept detection,intelligent multimedia systems,multimodal semantic indexing
Computer vision,Broadcasting,Information retrieval,Computer science,Search engine indexing,Keyword spotting,Ground truth,Artificial intelligence,Broadcast television systems,Text detection
Journal
Volume
Issue
ISSN
72
3
1573-7721
Citations 
PageRank 
References 
2
0.39
44
Authors
19