A family of information-theoretic algorithms for low-complexity discriminant feature selection in image retrieval

Feature selection remains a challenging problem for image retrieval due to the massive amounts of data involved in the retrieval problem and the need to perform learning on-line in response to user-interaction. Existing feature selection techniques have limited ability to satisfy these requirements, due to significant complexity, or dependence on assumptions, e.g. Gaussianity, that are unrealistic for multimedia data. In this paper, we exploit some results connecting information-theoretic feature selection techniques and the minimization of the Bayes classification error to develop a new family of feature selection algorithms. This family is shown to enable the design of discriminant feature spaces with low complexity, and provide explicit control over the trade-off between complexity and optimality in the information-theoretic.