Color is widely used for content-based image retrieval. In these applications the color properties of an image are characterized by the probability distribution of the colors in the image. These probability distributions are very often estimated by histograms although the histograms have many drawbacks compared to other estimators such as kernel density methods. In this article we investigate whether using kernel density estimators instead of histograms could give better retrieval results based on hue descriptors of color images. In this article we introduce the Fourier series coefficients as descriptors of hue distributions. We argue that under certain conditions these coefficients are optimal in a least squared error sense. We will also apply Parseval formula to compute the similarity of two distributions directly from these Fourier coefficients. Our experiments show that this modification of the kernel based similarity estimation has better retrieval performance than the histogram methods and we will also show that the method is insensitive to parameter changes as long as they are selected in a reasonable range.