Learning and Classification of Semantic Concepts in Broadcast Video

Broadcast video (news, documentaries, investi-gatory reports, etc.) is becoming an increasingly important medium for dissemination of information , sharing of knowledge and raising of public awareness on various issues. The ubiquity and open accessibility of broadcast video also gives it enormous potential as an important source of intelligence. However, technical challenges for automatically analyzing and understanding broadcast video content make it difficult to effectively tap this resource. In particular, manual processes for creating text-based metadata of broadcast video cannot keep up with the explosion of this content. Manual annotation is very costly and time consuming and often subjective, leading to incomplete and inconsistent annotations and poor system performance.

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