Fusion of self-organizing network and response analysis in object recognition with the hybrid evolutionary algorithm

摘要

The paper introduces the approach to the 2D automated content-based object recognition utilizing the hybrid evolutionary algorithm (HEA), self-organizing network (SON), and response analysis. When the object is distorted and spatially misregistered, the recognition system has to solve a nonlinear global search problem, i.e. find simultaneously the global positioning of the object and the parameters of its local distortion. The task is accomplished with the HEA using the operators of selection and recombination for the global search, and the accelerated Downhill simplex method for the local search. The algorithm minimizes the fitness formulated as the normalized least squared difference between the images of the scene and the object, and utilizes image local response. The response adequately captures the dynamics of the image transformation, which makes it particularly well suited for the evolutionary search. The response matrix of the object is evaluated and presented to a SON. The weights are computed during the iterative learning process. The resulting adaptive response map serves as the footprint of the object. The evolutionary procedure identifies potential matches for the object based on the response matrix. The local refining procedure uses the response map to accelerate local search in the vicinity of the potential optimal solution.