Abstract: A system for information retrieval within a database of large size includes a first module for extracting the descriptors associated with each object in the database, and for constructing a table containing the objects and the value of a descriptor associated with an object. The system also includes a second module for applying a number of classification algorithms, for each of the tables obtained from the module, a third module to fusion the results obtained from the module in order to determine, for each type of descriptor, a class number associated with an object, a fourth module for finding which column of a table is closest to the column obtained during the first fusion of the step, and for selecting the map that is closest contained in the table, or best map, and a fifth module to fusion the aggregate “best maps”, and applying an algorithm for searching for the best map to be transmitted to a display means.

Abstract: The updating method comprises selecting the best winning neuron and second best winning neuron, modifying the prototype vectors of the best winning neuron and the neurons located around the best winning neuron in the direction of the vector of the learning point (x(k)), determining the neighboring neurons (N(u*)) of the best winning neuron (u*) and, if the second best winning neuron (u**) is part of the neighboring neurons (N(u*)), increasing the valuation of the connection between the first and second best winning neurons. The updating method further involves reducing the valuation of each connection between the first best winning neuron and the direct neighboring neurons (N(u*)) other than the second best winning neuron (u**).

Abstract: A system for information retrieval within a database of large size includes a first module for extracting the descriptors associated with each object in the database, and for constructing a table containing the objects and the value of a descriptor associated with an object. The system also includes a second module for applying a number of classification algorithms, for each of the tables obtained from the module, a third module to fusion the results obtained from the module in order to determine, for each type of descriptor, a class number associated with an object, a fourth module for finding which column of a table is closest to the column obtained during the first fusion of the step, and for selecting the map that is closest contained in the table, or best map, and a fifth module to fusion the aggregate “best maps”, and applying an algorithm for searching for the best map to be transmitted to a display means.

Abstract: The updating method comprises selecting the best winning neuron and second best winning neuron, modifying the prototype vectors of the best winning neuron and the neurons located around the best winning neuron in the direction of the vector of the learning point (x(k)), determining the neighbouring neurons (N(u*)) of the best winning neuron (u*) and, if the second best winning neuron (u**) is part of the neighbouring neurons (N(u*)), increasing the valuation of the connection between the first and second best winning neurons. The updating method further involves reducing the valuation of each connection between the first best winning neuron and the direct neighbouring neurons (N(u*)) other than the second best winning neuron (u**).