Improved adaptive genetic algorithm based neural network image classification method

Abstract

The invention discloses an improved adaptive genetic algorithm based neural network image classification method. The method comprises the following steps: extracting texture features of sample images by adopting a gray-level co-occurrence matrix based feature extraction algorithm to obtain texture features of a training sample and a test sample; by taking the texture feature of the training sample as an input of an RBF neural network, training the RBF neural network with a genetic optimization based neural network learning method to generate a trained RBF neural network; and inputting the texture feature of the test sample into the trained RBF neural network, and performing an image classification test. For the deficiency that a k-means clustering algorithm and other algorithms are sensitive to initial value selection, the method can better avoid the ''prematurity'' convergence of a genetic algorithm, simplify the network structure of a neural network classifier and improve the generalization capability of the network and the correct classification efficiency of the images.

Description

technical field [0001] The invention relates to an image classification method, which belongs to the technical fields of intelligent computing and image processing, in particular to a neural network image classification method based on an improved self-adaptive genetic algorithm. Background technique [0002] Genetic Algorithm (GA) is a highly parallel random search algorithm developed by referring to the natural selection and evolution mechanism in the biological world. It provides a general framework for solving complex optimization problems and does not depend on the specific field of the problem. Strong robustness. It is widely used in the optimization of nonlinear systems and control schemes. There are some disadvantages in the standard genetic algorithm: when solving some practical complex problems, it is easy to appear "premature" convergence. When the solution in the group has not reached the optimal solution, the individuals become very similar and lose the ability...

AI Technical Summary

Problems solved by technology

There are some disadvantages in the standard genetic algorithm: when solving some practical complex problems, it is easy to appear "premature" convergence. When the solution in the group has not reached the optimal solution, the individuals become very similar and lose the ability of evolution, resulting in The population quickly converges to the local optimal solution instead of the global optimal solution, which greatly reduces the performance and optimization effect of the algorithm

[0003] Traditional image classification techniques are mostly based on large-scale calculations, and there is a serious contradiction between their calculation volume and calculation accuracy

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