Image retrieval and pattern recognition
Bo Tao, Bradley W. Dickinson · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1996
In this work, we study the relationship between content- based image retrieval and pattern recognition, by modeling the image retrieval process in a probabilistic method. A model called random image database will be presented, together with a retrieval quality measure called probability of self similar, which enables us to establish the link between image retrieval and pattern recognition. The main result is that such a quality measure is uniformly upperbounded by its pattern recognition counterpart using nearest neighbor rule, when only one training sample is available for each class. Therefore a feature measure having better performance in the one training sample per class case should be favored over features doing well in large training sample situations.