Unsupervised image division method based on multi-target immune cluster integration

Abstract

The invention discloses an unsupervised image division method based on a multi-target immune cluster integration technology, which mainly solves the problems of poor global optimization capability, single evaluation index, single division scheme form and difficult selection of a plurality of division schemes in the traditional technology. The method comprises the following steps of: (1) extracting gray scale information and wavelet energy information of an image to be divided; (2) sampling the image by using an area-based sampling policy to generate a test sample set; (3) selecting different characteristic vectors to form a plurality of test sample sets; (3) generating a primary division scheme by using an evolution cluster based on a multi-target immune algorithm; (5) integrating and learning an optimal division scheme in the primary division scheme; (6) marking the class of the pixel points of the image according to the selected division scheme; and (7) outputting the image division result. The invention has advantages of high average accuracy in image division and strong robustness and is applicable to the obtaining of image information and the division of image texture.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to an application of artificial intelligence technology in the field of image processing, specifically an unsupervised image segmentation method based on multi-objective immune clustering integration technology, which can be used for image understanding, and Target Recognition. Background technique [0002] With more and more image data, human interpretation has gradually withdrawn from the stage of history and replaced by machine interpretation. Image processing plays an important role related to the national economy and people's livelihood, and has become the focus of current research, and image segmentation is one of the basic issues in image processing. In the research and application of images, the target area of ​​interest can be found through image segmentation, which lays the foundation for the later classification and recognition of images, and the accuracy of targe...

AI Technical Summary

Problems solved by technology

[0008] However, in the existing technology, relatively fixed image features are always extracted, so only a batch of candidate segmentation schemes in a single form can be obtained, which makes it difficult to achieve the optimal final image segmentation result.

At the same time, in the existing technology, there is no effective and unified strategy to select the most suitable scheme from a batch of candidate segmentation schemes.

[0009] In summary, the existing image segmentation methods based on cluster analysis have the following four problems: (1) The global optimization ability is not strong; (2) The evaluation index is single; (3) The segmentation scheme is single; (4) Multiple segmentation Difficulty choosing a solution

If the above problems are not well resolved, the performance of the clustering analysis method for the data set will be very limited, and then the regional consistency of the image segmentation and the effective preservation of the edges cannot be guaranteed, which will eventually lead to the failure of the image segmentation method.

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