A Bayesian framework for content-based indexing and retrieval

Abstract

Summary form only given. One of the important requirements for practical retrieval systems is the ability to jointly address the issues of indexing and compression. By formulating query by example as a problem of Bayesian inference and establishing a link between probability density estimation and vector quantization, we have previously introduced a representation that leads to very efficient procedures for indexing and retrieval directly in the compressed domain without compromise of the coding efficiency. In this paper, we build on the potential of the Bayesian formulation to support sophisticated inference, to incorporate this representation in a very flexible indexing and retrieval framework that (1) leads to intuitive retrieval procedures, (2) can integrate different content modalities to eliminate some of the strongest limitations of the query by example paradigm, and (3) supports statistical learning of all the model parameters and can, therefore, be trained automatically.