Research On Improved Spectral Clustering Algorithm In The Application Of Image Segmentation

As an efficient and intelligent algorithm,spectral clustering algorithm has become a hot topic in clustering algorithms in recent years.It has been widely used in computer vision,image segmentation,text clustering,and data anomaly detection.However,the traditional spectral clustering algorithm applied to the image segmentation is very unsatisfactory.Therefore,an improved spectral clustering algorithm is introduced in this paper,and the performance of related algorithms is verified.In order to improve the defect that the traditional spectral clustering is applied to image segmentation with long running time and low segmentation precision,this paper proposes an improved spectral clustering image segmentation algorithm.The cosine distance is used instead of the Euclidean distance metric similarity matrix to avoid artificial errors caused by manually setting the scale parameters;in order to improve the segmentation accuracy,the texture information and spatial position are introduced into the feature vector of each pixel to describe the image more comprehensively.In the spectral mapping process,the Nystrom approximation method is used to approximate the similarity matrix and the principal eigenvectors to reduce the computational complexity;finally,the low-dimensional vector subgenerators obtained by the combination of the optimization algorithm and the optimized particle swarm optimization algorithm are used.The spatial clustering partitioning combines the advantages of the particle swarm algorithm and the algorithm,so that when dealing with large-scale data,it is better than a simple clustering algorithm,avoiding the emergence of local optimal conditions and accelerating the convergence speed.Experiments show that compared with the traditional spectral clustering,the algorithm has significantly improved running time and segmentation accuracy,and has a certain ability to suppress noise.