Embedded mixture modeling for efficient probabilistic content-based indexing and retrieval

Nuno Miguel Vasconcelos; Andrew B. Lippman

doi:10.1117/12.325807

5 October 1998 Embedded mixture modeling for efficient probabilistic content-based indexing and retrieval

Nuno Miguel Vasconcelos, Andrew B. Lippman

Proceedings Volume 3527, Multimedia Storage and Archiving Systems III; (1998) https://doi.org/10.1117/12.325807
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

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.

Citation Download Citation

Nuno Miguel Vasconcelos and Andrew B. Lippman "Embedded mixture modeling for efficient probabilistic content-based indexing and retrieval", Proc. SPIE 3527, Multimedia Storage and Archiving Systems III, (5 October 1998); https://doi.org/10.1117/12.325807

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