Neighbourhood learning meme image segmentation method based on standard cut

Abstract

The invention discloses a neighbourhood learning meme image segmentation method based on standard cut, mainly solving the problem that mutual learning between pixels is not used in the existing image segmentation method. The neighbourhood learning meme image segmentation method is performed by the steps as follows: firstly, conducting pixel-based two-dimensional random 0 / 1 coding on an image to form initial population and taking the standard cut as an overall measuring standard on the cut result of the image; then cloning individuals in the population according to cloning scale; conducting operations of variation, neighbourhood learning and clone selection in sequence on the cloned individuals; and finally storing an optimum image corresponding to an optimum standard cutting value as the segmentation result according to the standard cutting value in the individuals in the current population, and outputting an optimal image segmentation result according to the requirements of termination conditions. The neighbourhood learning meme image segmentation method is used for segmenting noise-free and noise images, has the advantages of being strong in region consistency, stable in convergence and optimal in overall and can be used for image identification and detection.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to digital image segmentation processing, in particular to a canonical cut-based neighborhood learning cultural gene image segmentation method, which can be used for image recognition and detection. Background technique [0002] Image segmentation plays a very important role in image analysis, image recognition, image detection, etc. It refers to separating and extracting a specific area from other parts of the image, that is, to distinguish the "foreground target" and "background". [0003] Image segmentation methods can be roughly divided into three categories according to different image characteristics: the first category is the threshold method, which uses a certain threshold reasonably according to the difference between the gray value of the target and the background in the image, which can effectively The target is separated from the background; the second type is th...

AI Technical Summary

Problems solved by technology

Although this method uses the two-step preprocessing of marking the watershed and extracting features from the image to obtain image segmentation results with better regional consistency, these two steps of preprocessing will seriously interfere with the subsequent evolution process, thereby affecting the effect of image segmentation.

Moreover, this cultural gene image segmentation method does not use the idea of ​​mutual learning among individuals in the population in the process of population evolution, so the obtained image segmentation results converge slowly.

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