Image dividing method based on immune multi-object clustering

Abstract

The invention discloses an image dividing method based on immune multi-object clustering, relating to the technical field of image processing, and mainly solving the problems that the conventional method has single evaluation index, and easily has bad region consistency and disorder boundary. The method comprises the following realization steps of: (1) extracting the characteristic of an image to be divided, and primarily dividing the image by controlling the watershed of a mark; (2) setting a running parameter and initializing the population of an antibody; (3) combining the locally-searched immune multi-objective optimizing method with the population of the antibody to obtain an approximate Pareto solution set; (4) selecting an optimal solution in the approximate Pareto solution set obtained in the step (3) according to a PBM index; and (5) marking an image pixel point according to a primary dividing result obtained in the step (1) and a clustering result obtained in the step (4) to obtain a final classifying result. The image dividing method has the advantages of good dividing result region consistency, being capable of keeping complete information, and having fast computation speed, and can be used for identifying an image object.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to an image segmentation method, and can be applied to target recognition. Background technique [0002] Applying intelligent computing technology to image segmentation is a popular research direction in the field of image segmentation in recent years, mainly including neural network, genetic algorithm, swarm intelligence algorithm and artificial immune system framework. Image segmentation is to divide an image into multiple regions or objects, which is a basic technique in image processing. From the perspective of segmentation results, the process of image segmentation is to assign a label to each pixel, which reflects the category of the pixel in the segmentation result. In the feature-based image segmentation method, each pixel is represented by its image features. As long as the labels of these features are found, the classification of pixels can be realized, so as to achiev...

AI Technical Summary

Problems solved by technology

The artificial immune system has the ability of self-adaptation, self-organization and self-learning, and can solve many complex problems that are difficult to solve by traditional methods

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