<p>Recent research proposed a Wasserstein-type distance specifically for Gaussian mixture models (GMMs). To extend this framework to identifiable mixture models with more general elliptically contoured distributions, it is essential that the marginal mixtures’ components are derived from the same distribution family while maintaining the marginal consistency property. In this paper, we introduce a simplified relaxed Wasserstein distance for identifiable mixtures of radially contoured distributions, where components may originate from different families. We demonstrate some properties of this distance and show that its definition does not require marginal consistency. We apply this distance in color transfer tasks and compare its performance with the Wasserstein-type distance for GMMs in an experiment. Our method requires fewer parameters than GMMs and the error of our method is comparable. The color distribution of our output image is more desirable.</p>