BP neural network image segmentation method and device based on adaptive genetic algorithm

Abstract

The invention relates to a BP neural network image segmentation method and device based on an adaptive genetic algorithm, and the method comprises the following steps: 1), analyzing a to-be-segmented image, and generating a training sample of a neural network; 2), setting the parameters of the neural network and population parameters, and carrying out the chromosome coding; 3), inputting the training sample for the training of the network, optimizing the weight value and threshold value of the network through employing a new adaptive genetic algorithm, adapting to the crossing and mutation operations, and introducing an adjustment coefficient; 4), inputting the to-be-segmented image, carrying out classifying of the trained neural network, and achieving the image segmentation. The device comprises a training sample generation module, a neural network structure determining module, a network training module, and an image segmentation module. The method introduces the adjustment coefficient which is related with the evolution generations, solves a problem that the individual evolution stagnates at the initial stage of population evolution, and also solves a problem of local convergence caused when the individual adaption degrees are close, thereby obtaining the neural network which can maximize representation of the image features, and achieving the more precise image segmentation.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a BP neural network image segmentation method and device based on an adaptive genetic algorithm. Background technique [0002] Image segmentation is the technology and process of accurately extracting specific and interesting objects in the image. As an important part of image analysis and computer vision systems, it determines the quality of digital image analysis and the quality of visual information processing results. , is a key step in image recognition and analysis. At present, image segmentation methods mainly include segmentation methods based on deformation models, segmentation methods based on regions, and segmentation methods based on statistics. [0003] When an image-designed segmentation algorithm is applied to a specific image, the effect is often unsatisfactory. Since various segmentation algorithms have certain pertinence and applicability, when it is n...

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Problems solved by technology

[0007] The present invention provides a BP neural network image segmentation method and device based on an adaptive genetic algorithm, aiming to solve the problem that the existing image segmentation method is prone to individual evolutionary stagnation, local convergence and low segmentation accuracy of the neural network method in the early stage of population evolution , the problem of slow training speed

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