5. An active learning system as set forth in claim 3 , wherein the learning-feature graph-fingerprinting module forms a fuzzy graphical model (FGM) of object candidates to fingerprint the object by representing three-dimensional (3D) spatial relationships between salient object points and corresponding two-dimensional (2D) view-dependent feature vectors.
5. An active learning system as set forth in claim 3 , wherein the learning-feature graph-fingerprinting module forms a fuzzy graphical model (FGM) of object candidates to fingerprint the object by representing three-dimensional (3D) spatial relationships between salient object points and corresponding two-dimensional (2D) view-dependent feature vectors. 6. An active learning system as set forth in claim 5 , wherein the FGM utilizes an algorithm for sub-graph matching to minimize an objective function, wherein the objective function is defined by: J ( U , C ) = ∑ i = 1 n + 1 ⁢ ∑ j = 1 m + 1 ⁢ u ij 2 ⁢ f ⁡ ( c ij ) + η ⁢ ∑ i = 1 n + 1 ⁢ ∑ j = 1 m + 1 ⁢ u ij ⁡ ( 1 - u ij ) , where: n is a constant that controls the relative influence of the two terms in the minimization process; c ij represents the absolute compatibility between nodes f in a graph A (i.e., graph A is a stored graph representing a candidate object) and j in a second graph (i.e., graph B represents an input object that is matched against graph A) (given the fuzzy assignments U), taking into account the attributes of the edges incident on nodes i and j and those of the neighboring nodes of i and j; C =[c ij ] is a compatibility matrix; u ij is an element of an assignment matrix U, representing a degree to which node i in graph A matched node j in graph B; n and m are the number of nodes in graphs A and B respectively; function f( ) is similar to a distance function and takes on high values if c ij is low and vice versa; compatibilities c ij depend on U; assignments U depend on the compatibilities C; and where U and C are updated in an alternating fashion, giving rise to a relaxation process.