Research On Applications Of Fireworks Algorithm In Image Processing

With the advent of the information age,application of digital image processing has become increasingly widespread;meanwhile,the requirement for the accuracy and efficiency of the image processing is also getting higher and higher.However,there is a huge of data in images,moreover,the computational complexity is high during the process of image processing,a lot of real-time application requirements cannot be solved quickly and efficiently by traditional algorithms.Hence,the application of the new swarm intelligence algorithm in the image processing method has been widely concerned.Fireworks algorithm(FWA)is a novel heuristic algorithm with a few parameters,global optimization ability,which is not easy to fall into the local optimal solution,can carry out parallel search and has the advantages of diversity,explosive.It has been applied for wide fields,such as filter design,power system reconfiguration,etc.At present,there are a few studies about application of FWA in image processing.This thesis employs FWA for seeking the multilevel thresholds for image segmentation,selecting the optimal textual feature subset with Gabor Wavelet and the best parameters of SVM.The main work is as follows:1.Application of FWA in multi-threshold image segmentation is studied and implemented.Multilevel Otsu method and minimum cross entropy method is utilized for image thresholding,and FWA is used to obtain the optimal thresholds quickly in the thesis.Further,FWA is contrasted with some other classic evolutionary algorithm.The results show that it is accurate and effective to apply fireworks algorithm in multi-threshold image segmentation.The standard deviation of the fitness value of FWA is less than other algorithms after many times running,which displays that the stability of the FWA is stronger and solves the problem with bad efficiency and robustness of the traditional approach for multilevel image thresholding,which is an effective method for image segmentation.2.A textural image feature selection approach based on the binary fireworks algorithm is proposed.First features of texture images are extracted through Gabor wavelet transform,and then the binary coded FWA is used to select the optimal wavelet textural feature subset,it does not affect the classification accuracy with the reduced feature subset.3.Application of FWA for optimizing parameters support vector machine(SVM)algorithm is studied and implemented.It solves the parameter optimization problem of SVM as a combinatorial optimization problem.First,some data from UCI data repository is used to verify the performance of FWA;then some textural images with the feature of Gabor wavelet are classified.Results show it is practical to apply FWA to optimize parameters of support vector machine(SVM)performance and the classification accuracy is satisfactory.All in all,FWA is applied to multilevel thresholds image segmentation,image feature selection and image classification in the thesis.The experimental results show that the fireworks algorithm has good performance in image processing and has good prospects in the field of image processing.