| In the field of pattern recognition,cluster analysis,as an important method,is applied to data analysis,feature extraction,and classification in addition to the clustering problem itself.However,in the cluster analysis problem,due to the poor quality of the data in the actual scene,there are often cases of noise,abnormalities,and poor data separability.Traditional clustering methods such as kmeans,gmm clustering effects will be disturbed.The clustering algorithm based on fuzzy theory applies the fuzzy set theory to the clustering algorithm,also called soft clustering algorithm,to express the concept that the data does not belong to only one cluster.The theory of fuzzy clustering algorithm makes people begin to use fuzzy sets to express the uncertainty in the clustering problem,but the practical problems encountered by the clustering algorithm have not been solved.In recent years,some people have used the neutrosophic set in the clustering algorithm,hoping to deal with the noise and clustering boundary problems in the clustering algorithm.In response to these problems,this paper introduces neutrosophic set into the clustering algorithm and mainly performs the following work:(1)In this paper,a clustering algorithm framework based on the neutrosophic set is designed.Through the idea of neutrosophic set and the cost function of the clustering algorithm,the noise,abnormal point samples and boundary ambiguous samples in the actual data set are processed in a targeted manner.Based on this,this paper proposes an improved NGMM algorithm for GMM model.In addition,this paper proposes the design of the neutrosophic sets I domain and F domain under the GMM model,and analyzes the possible problems in the actual scenario,such as normalized problems,hyperparametric sensitive issues,and give corresponding improvements.Compared with traditional GMM algorithm and fuzzy clustering algorithm for processing noise,the proposed algorithm can obviously improve the clustering and classification performance of clustering algorithm under noise and ambiguous data scenes.(2)This paper applies the NGMM model to the actual image scene: video foreground extraction to cope with the noise and complex environmental factors in video data.In the video foreground extraction algorithm based on the NGMM model,this paper integrates the neutrosophic sets into the online EM algorithm to update the parameters and weights of each Gaussian component.The algorithm in this paper will make the rate of parameter update slow,but it can maintain goodalgorithm performance in the case of poor video data quality. |
