Artificial immunization non-supervision image classification method based on manifold distance

Abstract

The invention discloses an artificial immunization non-supervision image classification method based on manifold distance and relates to the technical field of image processing. The specific process includes: (1) inputting an image to be classified and setting an initialization parameter to generate initialized antibody population; (2) based on the manifold distance, classifying the category of the sample point of the image to be classified and calculating the affinity of the antibody population; (3) carrying out clonal proliferation operation on the antibody population; (4) carrying out clonal variation operation on the antibody population after clonal proliferation; (5) classifying the category of the image to be classified according to a code of the antibody population after clonal variation and calculating the affinity of the antibody population; (6) carrying out clonal selection operation on the antibody population according to the antibody affinity; and (7) according to set maximum iterations, judging the stop condition of the category classification result of the image to be classified and determining the final classification result. The classification method has the advantages of low sensitivity of image data structure, non-supervision execution, good classification effect and strong robustness, and can be applied to the target identification in the field of image processing.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image classification method, which can be used for object recognition in image processing. Background technique [0002] Image processing is an interdisciplinary field. With the continuous development of computer science and technology, image processing and analysis has gradually formed its own scientific system. Image classification is the process of dividing an image into several regions according to a certain criterion, requiring the pixels in the same region to have a certain consistency, and there is no such consistency between pixels in different regions. Image classification methods have always been a research hotspot in image processing and analysis, and also a key issue in computer vision, especially unsupervised image classification methods based on texture features are research hotspots in recent years. [0003] Texture features are important a...

AI Technical Summary

Problems solved by technology

The commonly used similarity measure criterion is Euclidean distance, which has a good effect on data samples with Gaussian distribution structure, but it cannot reflect the distribution structure of complex image data samples, so the similarity measure based on Euclidean distance The performance measurement criterion has defects such as high sensitivity to image data structure, poor classification effect and weak robustness.

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