A probabilistic architecture for content-based image retrieval
The design of an effective architecture for content-based retrieval from visual libraries requires careful consideration of the interplay between feature selection, feature representation, and similarity metric. We present a solution where all the modules strive to optimize the same performance criteria: the probability of retrieval error. This solution consists of a Bayesian retrieval criteria (shown to generalize the most prevalent similarity metrics in current use) and an embedded mixture representation over a multiresolution feature space (shown to provide a good trade-off between retrieval accuracy, invariance, perceptual relevance of similarity, and complexity). The new representation extends standard models (histogram and Gaussian) by providing simultaneous support for high-dimensional features and multi-modal densities and performs well on color texture, and generic image databases.