Texture image segmentation method based on Lamarck multi-target immune algorithm

Abstract

The invention discloses a texture image segmentation method based on Lamarck multi-target immune algorithm, mainly aiming at solving the problems of high operational data quantity, weak global optimization capability, one-sided evaluation index and poorer local searching capability in the prior art. The texture image segmentation method comprises the steps of: (1) extracting image grey-scale information and image small wave energy information; (2) based on watershed pre-segmentation, generating a test sample set for image sampling; (3) using the Lamarck multi-target immune algorithm for carrying out data clustering on the test sample set, and generating a data clustering scheme sets (4) according to the Minkowski index value, selecting the most satisfied data clustering scheme; (5) according to the selected data clustering scheme, marking image pixel point category attribution; and (6) outputting the image segmentation result. The texture image segmentation method has the advantages of low operational data quantity, lower calculation complexity, high image segmentation average accuracy rate and excellent segmentation result, and can be used for image information acquisition and image texture partition.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to image segmentation, in particular to a texture image segmentation method based on a Lamarck multi-object immune algorithm, which can be used for image understanding and object recognition. Background technique [0002] After entering the 21st century, there are more and more image data, and machine interpretation has replaced human interpretation. Image processing is increasingly playing an important role related to the national economy and people's livelihood, and has become the focus of current scientific research. Image segmentation is one of the fundamental problems in image processing. Image engineering can usually be divided into three levels, image processing, image analysis and image understanding. As an important step in the process from image processing to image analysis, image segmentation is the basis for further image understanding. The task of image segment...

AI Technical Summary

Problems solved by technology

[0009] In summary, the existing image segmentation methods based on cluster analysis have the following three problems: (1) the amount of computing data is too large; (2) the global optimization ability is not strong; (3) the evaluation index is single; (4) local search lack of ability

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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