Similarity-based access to image databases assumes one or more similarity models. Although this assumption affects the retrieval precision of a system considerably, it is rarely described explicitly. Furthermore, because the similarity model is typically hard-coded into the system, it is very difficult if not impossible to use such a system for applications that do not fit the same similarity model. In this work, we develop a framework for designing similarity-based image access systems that are driven by human perception, and hence can be tailored for multiple, diverse applications. The driving components of the approach are Principal Components-based feature selection, perception modeling via psychophysical experiments and Genetic Algorithm-driven distance function optimization. While our framework is general and flexible, we demonstrate the application in a particular image access scenario: Shape-based retrieval of skin lesion images. The experimental results show that, by incorporating human perception of similarity into the system, retrieval performance may be significantly improved.