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Proceedings Paper

Bayesian representations and learning mechanisms for content-based image retrieval
Author(s): Nuno Miguel Vasconcelos; Andrew B. Lippman
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Paper Abstract

We have previously introduced a Bayesian framework for content-based image retrieval that relies on a generative model for feature representation based on embedded mixtures. This is a truly generic image representation that can jointly model color and texture and has been shown to perform well across a broad spectrum of image databases. In this paper, we expand the Bayesian framework along two directions.

Paper Details

Date Published: 23 December 1999
PDF: 12 pages
Proc. SPIE 3972, Storage and Retrieval for Media Databases 2000, (23 December 1999); doi: 10.1117/12.373579
Show Author Affiliations
Nuno Miguel Vasconcelos, MIT Media Lab. (United States)
Andrew B. Lippman, MIT Media Lab. (United States)


Published in SPIE Proceedings Vol. 3972:
Storage and Retrieval for Media Databases 2000
Minerva M. Yeung; Boon-Lock Yeo; Charles A. Bouman, Editor(s)

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