Abstract:

To solve the issues of lack of processing nonlinear data and noise when using subspace clustering algorithms in image segmentation tasks, an image segmentation algorithm based on nonconvex low-rank subspace clustering is proposed. First, the adaptive morphological reconstruction seed segmentation method is used to perform the point-by-point maximum operation on the gradient image. The original image is pre-segmented into superpixel images of different area sizes, which remedies the over-segmentation defect of superpixel segmentation methods. Then, the color features of superpixel block are extracted and stacked into a data matrix, and are further input into the multi-kernel subspace clustering algorithm.; Next, the coefficient matrix is solved according to the subspace representation, and the affinity matrix is constructed. Finally, the affinity matrix is input to the spectral clustering to obtain the final segmentation results. The results of comparison experiments on public data sets show that the proposed method achieves the best clustering performance and segmentation effect.

Key words: subspace clustering, image segmentation, multiple-kernel, superpixel