1. A method comprising: providing a universal generative model of local descriptors; adapting the universal generative model to a first camera to obtain a first camera-dependent generative model using local descriptors extracted from each of a set of training images captured by the first camera; adapting the universal generative model to a second camera to obtain a second camera-dependent generative model using local descriptors extracted from each of a set of training images captured by the second camera or using the universal generative model as the second camera-dependent generative model; from a first test image captured by the first camera, extracting a first image-level descriptor, the first image-level descriptor being a fixed-length vectorial representation of the first test image generated by aggregating local descriptors extracted from the first image into a fixed-length representation using the first camera-dependent generative model; from a second test image captured by the second camera, extracting a second image-level descriptor, the second image-level descriptor being a fixed-length vectorial representation of the second test image generated by aggregating local descriptors extracted from the second image into a fixed-length representation using the second camera-dependent generative model; computing a similarity between the first image-level descriptor and the second image-level descriptor; and outputting information based on the computed similarity, wherein at least one of the adapting the universal generative model to the first and second cameras, extracting the first and second image-level descriptors and the computing of the similarity is performed with a computer processor.