Research on spectral clustering infrared image segmentation algorithm based on improved sparse matrix

In the infrared image segmentation, spectral clustering needs to calculate the similarity matrix between pixel points, the amount of data is large and the calculation is time-consuming. To solve this problem, an improved spectral clustering infrared image segmentation algorithm based on improved sparse matrix is proposed. The algorithm combines the feature of the whole image with the relationship between pixels, and then convinces the network to extract the infrared image feature information through convolution, and uses the selected feature information to construct the sparse similarity matrix, and completes the segmentation by combining the spectral clustering method. Experimental results show that this algorithm can effectively reduce the computational complexity of spectral clustering and effectively improve the segmentation result of the target area of infrared images.