Image Segmentation Algorithm Under The Framework Of Membrane Computing

Image segmentation is a key part of image processing,and the quality of its results will directly affect the subsequent steps,which is a classic research topic.Image segmentation is widely used in all walks of life,such as medicine,public security and so on.With the progress of the times,the technology is updated,and many new theories and ideas are added in this field,resulting in many new segmentation algorithms.The Gaussian mixture model is a commonly used model for data analysis.Image segmentation algorithm based on Gaussian mixture model is one of soft clustering image segmentation.Expectation maximization(EM)algorithm is a commonly used algorithm for solving parameters in Gaussian mixture model.For the EM algorithm,different initial values often lead to different results.In order to solve this problem,this paper considers introducing membrane computer system to improve this defect.Secondly,in view of the problem that the Gaussian component needs to be given manually in the image segmentation algorithm based on the Gaussian mixture model,an image reconstruction method is proposed,which automatically selects the Gaussian component based on the WIRC value.Finally,in view of the difference between the human eye and the algorithm for the image segmentation module,the membrane spectrum clustering algorithm is used for merging.On the other hand,with the replacement of cameras,screens and other hardware devices,image information becomes more abundant.How to deal with large-scale and high-resolution images quickly and effectively while obtaining high-definition visual effects also follows.In response to the new requirements of image segmentation field,super-pixel segmentation method came into being.In this paper,the principle of membrane computing is introduced into the super-pixel segmentation,in order to improve the performance of the super-pixel segmentation algorithm.The main innovative work of this paper is summarized as follows:(1)To solve the problem of color image segmentation,an image segmentation method based on the combination of Gaussian mixture model and membrane spectrum clustering is proposed.Firstly,the initial image is processed by the membrane clustering algorithm,and the obtained value is used as the initial value of the EM algorithm of the Gaussian mixture model to build the initial small area for image modeling;then,the weighted image reconstruction method is used to automatically obtain the optimal number of Gaussian components;finally,the membrane spectrum clustering is used to merge the Gaussian components to get the final image segmentation results.The evaluation results on UCI data set and COREL 1000 image set show the effectiveness of this method and the improved segmentation effect.(2)A new method of super-pixel segmentation based on membrane computing is proposed by introducing the mechanism of membrane computing into the super-pixel segmentation.The problem of super-pixel segmentation is regarded as an optimization problem.Therefore,a tissue-like P system is designed as the computing framework.The tissue-like P system is used to find the optimal super-pixel center,where for each local area,the centers of super-pixels are tracked by selecting q groups of cells.In other words,candidate particles are used to approach the real super pixel center,so as to achieve higher regional similarity.Experimental results show the availability and effectiveness of the method.