Linear and nonlinear (i.e., kernel) discriminant analysis have been proposed to address the difficulties of the small sample problem, the curse of dimensionality, and the multi-modality of image data distribution in content-based image retrieval (CBIR). The existing discriminant analysis is implemented either in a regular way, such as MDA (multiple discriminant analysis), or in a biased way, such as biased discriminant analysis (BDA). A rich set of parameterized discriminant analysis is proposed as an alternative to the regular MDA and BDA when taking regularization into account to avoid the singularity of the scatter matrices. Extensive experiments are carried out for performance evaluation and the results show the superior performance of the parameterized discriminant analysis over regular MDA and BDA for both linear and nonlinear settings.