With the explosion of digital media and online services, a key challenge in
the area of media management is automation of content annotation, indexing,
and organization for efficient media access, search, retrieval, and browsing. A
major failing of current media annotation systems is the semantic gap — the
incompatibility between the low-level features that can be currently computed
automatically to describe media content and the high-level meaning associated
with the content by users in media search and retrieval. This inevitably leads to
the problem of content management systems returning media clips that are simi-lar
to one another in terms of low-level descriptions, but are completely different
in terms of semantics sought by the users in their search. This chapter introduces
Computational Media Aesthetics as an approach to bridging the semantic gap,
outlines its foundations in media production principles, presents a computational
framework to deriving high-level semantic constructs from media, and describes
the structure of this collection.
By: Chitra Dorai, Svetha Venkatesh
Published in: RC22656 in 2002
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