Multimodal concept detection in broadcast media: KavTan
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||46|
|Journal||Multimedia Tools and Applications|
|Publication status||Published - Oct 2014|
|Publication type||A1 Journal article-refereed|
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.
- Intelligent multimedia systems, Concept detection, Broadcast video indexing, Multimodal semantic indexing, RELEVANCE FEEDBACK, IMAGE RETRIEVAL, AUDIO CLASSIFICATION, FEATURES, RECOGNITION, ALGORITHMS, VIDEO, NEWS