1. A method to recognize model instances in an input signal, the method comprising the steps of: storing in a long-term memory a hierarchical collection of models, wherein: the instances of each model are represented by an ensemble of patterns; a non primitive pattern, defined on a domain and representative of an instance of a non-primitive model from said hierarchical collection has a fragmentation comprising a clique of local patterns; and wherein each local pattern, defined on a sub-domain of said domain, is representative of a local model from said hierarchical collection and provides an approximate representation of said non-primitive pattern within said sub-domain; generating, by a processing means, a plurality of descriptors wherein each of said descriptors specifies an estimated pattern representative of an instance of a model detected in said input signal; storing said descriptors in a short-term memory; extracting from said short-term memory, by said processing means, a clique of descriptors which specifies a clique of estimated patterns; selecting a model to be recognized from said hierarchical collection; and generating, by said processing means, and by using said clique of descriptors as auxiliary descriptors, a new descriptor that specifies a new pattern, wherein said new pattern has a fragmentation given by said clique of estimated patterns and is representative of an instance of said model to be recognized.
3. The method of claim 1 , wherein said non-primitive pattern is a weighted sum of component patterns representative of instances of component models from said hierarchical collection, and wherein any one of said component patterns has a small interference signal inside the domain of any other one of said component patterns.