Graphic image recognition and matching method based on genetic programming algorithms of novel coding modes

Abstract

The invention belongs to the technical field of image processing, particularly discloses a graphic image recognition and matching method based on genetic programming algorithms of novel coding modes, and aims to acquire more suitable features in image matching through novel algorithms so as to increase retrieval accuracy in image recognition and matching. The method includes the steps of 1, setting parameters, initializing a population and selecting matching modes; 2, calculating population fitness and performing genetic operations including crossing, variation, self-crossing and self-exchanging; 3, optimizing individuals after genetic operations, and performing local retrieval; 4, further optimizing the population and judging whether or not evolution ends; 5, decoding an individual tree to obtain modes of extracting new features so as to obtain new image features; 6, outputting an image matching model according to the new image features and the set matching modes. The method has the advantages that the training model is generated and image recognition and matching accuracy can be increased effectively.

Description

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AI Technical Summary

Benefits of technology

This patented technology uses an improved method for creating models that accurately recognize images from databases containing data similarities or dissimilarity patterns. These methods involve learning how well these templates match with those stored on their own dataset before testing them against other datasets. By comparing this trained template with corresponding ones found during previous experiments, it becomes possible to make better predictions about future matches based upon similarity scores obtained through machine-learning techniques like neural networks.

Problems solved by technology

The technical problem addressed in this patented text relates to improving visual or audio data recognition capabilities without requiring human laborious effort on manually annotated tags that may lead to incorrect matches due to factors like variations over time such as changes caused by weather patterns or other environmental influences.

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