The design of end-to-end optimal image retrieval systems
In the last few years, image retrieval has evolved from early simplistic solutions (such as histogram intersection) to more principled approaches that can be shown to be optimal under sensible criteria for the retrieval problem. We discuss a decision theoretic formulation that enables the design of systems where all components are optimized with respect to the same end-to-end performance criteria: the minimization of the probability of retrieval error. This discussion includes both a theoretical characterization of how the probability of error is affected by the design of the basic components of a retrieval system (feature transformation, feature representation, and similarity function) and experimental evidence of how various design choices impact the retrieval performance.