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Proceedings Paper

Embedded mixture modeling for efficient probabilistic content-based indexing and retrieval
Author(s): Nuno Miguel Vasconcelos; Andrew B. Lippman
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Paper Abstract

By formulating content-based retrieval as a problem of Bayesian inference we have previously developed a retrieval framework with various interesting properties: (1) allows the incorporation of prior beliefs about image relevance in the retrieval process, (2) leads to simple and intuitive mechanisms for combining information from several modalities, such as images, audio, and text during retrieval, (3) provides support for the development of interfaces that learn from user interaction, (4) allows retrieval directly from compressed bitstreams, and (5) lends itself to the construction of indexing structures which can also be computed as a side effect of the compression process.

Paper Details

Date Published: 5 October 1998
PDF: 10 pages
Proc. SPIE 3527, Multimedia Storage and Archiving Systems III, (5 October 1998); doi: 10.1117/12.325807
Show Author Affiliations
Nuno Miguel Vasconcelos, MIT Media Lab. (United States)
Andrew B. Lippman, MIT Media Lab. (United States)


Published in SPIE Proceedings Vol. 3527:
Multimedia Storage and Archiving Systems III
C.-C. Jay Kuo; Shih-Fu Chang; Sethuraman Panchanathan, Editor(s)

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