Improved Spectral Clustering Clothing Image Segmentation Algorithm Based on Sparrow Search Algorithm

In the process of clothing image researching, how to segment the clothing quickly and accurately and retain the clothing style details as much as possible is the basis of subsequent image analysis. Spectral clustering clothing image segmentation algorithm is a common method in the process of clothing image extraction. However, the traditional model requires high computing power and is easily affected by the initial center of clustering. It often falls into local optimization. Aiming at the above two points, an improved spectral clustering clothing image segmentation algorithm is proposed in this paper. The Nystrom approximation strategy is introduced into the spectral mapping process to reduce the computational complexity. In the clustering stage, this algorithm uses the global optimization advantage of the particle swarm optimization algorithm and selects the sparrow search algorithm to search the optimal initial clustering point, to effectively avoid the occurrence of local optimization. In the end, the effectiveness of this algorithm is verified on clothing images in each environment.