1. A method of compressing visual descriptors from at least one image by exploiting redundancy of natural image descriptors, comprising: extracting the visual descriptors from at least one image, said visual descriptors describing key points in images; creating model parameters of a generative probabilistic model from the extracted visual descriptors in a maximum likelihood sense; quantizing and encoding said model parameters; quantizing said extracted visual descriptors; and, applying a model-based arithmetic encoding to said quantized extracted visual descriptors using said encoded model parameters exploiting redundancy of the visual descriptors within the at least one image for compression of the visual descriptors.
4. The method of claim 1 , said encoding of said quantized extracted visual descriptors comprising: associating each said visual descriptor with a corresponding Gaussian mixture model component for which the likelihood of said visual descriptor is maximum; rearranging said visual descriptors by order of Gaussian mixture model component indices so that the visual descriptors are non-decreasing; encoding Gaussian mixture model component indices using a predictive entropy coding scheme; and encoding each said visual descriptor using a multivariate Gaussian-based arithmetic coding.