This paper presents a new approach to ranking relevant images for retrieval. Distance in the feature space associated with a kernel is used to rank relevant images. An adaptive quasiconformal mapping based on relevance feedback is used to generate successive new kernels. The effect of the quasiconformal mapping is a change in the spatial resolution of the feature space. The spatial resolution around irrelevant samples is dilated, whereas the spatial resolution around relevant samples is contracted. This new space created by the quasiconformal kernel is used to measure the distance between the query and the images in the database. An interesting interpretation of the metric is found by looking at the Taylor series approximation to the original kernel. Then the squared distance in the feature space can be seen as a combination of a parzen window estimate of the squared Chi-squared distance and a weighted squared Euclidean distance. Experimental results using real-world data validate the efficacy of our method.