The Firework Algorithm Optimizes The Color Image Segmentation Of Normalized Cut

Image segmentation is a computer vision technology and one of the key steps in image processing.Image segmentation is not simply dividing the image into several non-intersecting sub-regions,but the process of separating the background and obtaining the object of interest.It can simplify the image processing and reduce the impact of irrelevant information on the study of the object.Feature calculation,target recognition and other research based on image segmentation results have a direct relationship with the segmentation quality.Good segmentation results can improve the foundation of further analysis and understanding of the image.Therefore,improving the accuracy of image segmentation results has been a hot issue.The solving process of the Normalized Cut segmentation method is actually an NP problem,and the traditional calculation methods have problems of long time and low accuracy of results.Swarm intelligence optimization algorithm can solve problems such as precision by simulating biological information interaction optimization.Fireworks algorithm is one of the new swarm intelligence optimization algorithms in recent years.In this paper,the fireworks algorithm is integrated with the Normalized Cut segmentation method and applied in the color image segmentation.The following is the main research content of this paper:(1)The adaptive fireworks algorithm optimizes the color image segmentation method of the Normalized Cut.The specific operation process is as follows:FCM clustering is carried out on the segmented image to generate a number of maximum regions,and the results are mapped into undirected weighted images.The optimal value of Ncut is solved by the adaptive fireworks algorithm,and color image segmentation is guided by the optimization results.The explosion radius of the adaptive fireworks algorithm changes dynamically in the optimization process.The initial search radius is large and exploratory search is carried out in the feasible region.As the radius decreases gradually with the iterative process,the mining search is focused on the optimal region to further improve the accuracy of the optimization results.Experiments show the effectiveness of adaptive segmentation algorithm.(2)The fireworks algorithm based on tabu search optimizes the color image segmentation method of the Normalized Cut.The tabu table is mainly added in the traditional fireworks algorithm,which is used to store the local optimal value acquired in the iteration process,and mark according to the results of subsequent iterations.The information stored in the tabu can be searched without searching or selectively to avoid falling into the local optimal.The optimized Normalized Cut algorithm based on tabu search guides the color image segmentation,and the experimental results show the effectiveness of the improved approach.(3)The optimized Normalized Cut segmentation methods are applied in agriculture.In this paper,a segmentation system based on the optimized Normalized Cut segmentation methods based on a collection of multiple methods is provided.The core parameters of the corresponding optimization methods can be adjusted according to the relevant information of images,and the segmentation process is reflected by FCM block diagram,Ncut line diagram in the iterative process and the segmentation result diagram.In this paper,the pomegranate garden as an example,the collection of images in the pomegranate garden for segmentation,pomegranate fruit as a target object extracted from the image,the segmentation results can be used as an important basis for the subsequent calculation of pomegranate growth parameters,has more important significance in agricultural automation.