Information-conserving object recognition

Following the theory of statistical estimation, the problem of recognizing objects imaged in complex real-world scenes is examined from a parametric perspective. A scalar measure of an object's complexity, which is invariant under affine transformation and changes in image noise level, is extracted from the object's Fisher information. The volume of Fisher information is shown to provide an overall statistical measure of the object's recognizability in a particular image, while the complexity provides an intrinsically physical measure that characterizes the object in any image. An information-conserving method is then developed for recognizing an object imaged in a complex scene. Here the term information-conserving means that the method uses all the measured data pertinent to the object's recognizability, attains the theoretical lower bound on estimation error for any unbiased estimate, and therefore is statistically optimal. This method is then successfully applied to finding objects imaged in thousands of complex real-world scenes.