A computer-implemented processing method for retrieval of similar images comprising: adapting a Gaussian mixture model derived from a training set of images and including a plurality of Gaussian distribution components to a first image to generate a first other Gaussian model including a plurality of first other Gaussian distribution components having one-to-one correspondence with the plurality of Gaussian distribution components; adapting the Gaussian mixture model including the plurality of Gaussian distribution components to a second image to generate a second other Gaussian model including a plurality of second other Gaussian distribution components having one-to-one correspondence with the plurality of Gaussian distribution components; wherein the first other Gaussian model is defined by p x = ∑ i N α i p i x and the second other Gaussian model is defined by q x = ∑ i N β i q i x, where p(x), q(x), α i and β i are the mixture densities, the weights and the distribution functions of the first other Gaussian mixture model and the second other Gaussian mixture model, respectively, and N is the number of Gaussian distribution components of the Gaussian mixture model derived from the training set of images; performing component-by-component comparison of the plurality of first other Gaussian distribution components and the plurality of second other Gaussian distribution components obtained by adaptation of the Gaussian mixture model to the second image and having one-to-one correspondence with the plurality of first other Gaussian distribution components; and generating a similarity measure for the first and second images based on the component-by-component comparison. adapting a Gaussian mixture model derived from a training set of images and including a plurality of Gaussian distribution components to a first image to generate a first other Gaussian model including a plurality of first other Gaussian distribution components having one-to-one correspondence with the plurality of Gaussian distribution components; adapting the Gaussian mixture model including the plurality of Gaussian distribution components to a second image to generate a second other Gaussian model including a plurality of second other Gaussian distribution components having one-to-one correspondence with the plurality of Gaussian distribution components; wherein the first other Gaussian model is defined by p x = ∑ i N α i p i x and the second other Gaussian model is defined by q x = ∑ i N β i q i x, where p(x), q(x), α i and β i are the mixture densities, the weights and the distribution functions of the first other Gaussian mixture model and the second other Gaussian mixture model, respectively, and N is the number of Gaussian distribution components of the Gaussian mixture model derived from the training set of images; performing component-by-component comparison of the plurality of first other Gaussian distribution components and the plurality of second other Gaussian distribution components obtained by adaptation of the Gaussian mixture model to the second image and having one-to-one correspondence with the plurality of first other Gaussian distribution components; and generating a similarity measure for the first and second images based on the component-by-component comparison.