NDLI: Semantic representation: search and mining of multimedia content

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Semantic representation: search and mining of multimedia content

Content Provider	ACM Digital Library
Author	Naphade, Milind R. Smith, John R. Natsev, Apostol Paul
Abstract	Semantic understanding of multimedia content is critical in enabling effective access to all forms of digital media data. By making large media repositories searchable, semantic content descriptions greatly enhance the value of such data. Automatic semantic understanding is a very challenging problem and most media databases resort to describing content in terms of low-level features or using manually ascribed annotations. Recent techniques focus on detecting semantic concepts in video, such as indoor, outdoor, face, people, nature, etc. This approach works for a fixed lexicon for which annotated training examples exist. In this paper we consider the problem of using such semantic concept detection to map the video clips into semantic spaces. This is done by constructing a model vector that acts as a compact semantic representation of the underlying content. We then present experiments in the semantic spaces leveraging such information for enhanced semantic retrieval, classification, visualization, and data mining purposes. We evaluate these ideas using a large video corpus and demonstrate significant performance gains in retrieval effectiveness.
Starting Page	641
Ending Page	646
Page Count	6
File Format	PDF
ISBN	1581138881
DOI	10.1145/1014052.1014133
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Trecvid Model vectors Semantic indexing
Content Type	Text
Resource Type	Article

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