Bayesian representations and learning mechanisms for content-based image retrieval
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.