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 judgments, 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.
We review recent advances in image retrieval. The two fundamental components of a retrieval system, representation and learning, are analyzed. Each component is decomposed into its constituent building blocks: features, feature representation, and similarity function for the representation; short-and long-term procedures for learning. We identify a series of requirements for each of the sub-areas, e.g. optimality, invariance, perceptual relevance, computational tractability, and point out various approaches proposed to satisfy them. Several open problems are also identified.
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