Tensor-based multi-feature affinity graph learning for natural image segmentation.

In the previous image segmentation methods based on affinity graph learning, it is difficult to obtain clear contour boundaries in the process of image preprocessing, over-segmentation leads to the separation of local regions and most traditional affinity graph learning methods cluster and segment with a single feature of natural images, ignoring the associative characteristics of multiple types of features and cannot effectively utilize the useful information of multiple features of natural images. Aiming at such problems, this paper proposes a tensor-based multi-feature affinity graph learning method for natural image segmentation (TRMFAL). First, the adaptive morphological rebuilding watershed transform is applied to original natural image, the obtained superpixel image contains a lot of boundary contour information, and the fusion of local regions is relatively good; secondly, extract multi-class features from the superpixel blocks in the superpixel image, effectively utilize the features of different characteristics, and integrate them into a multi-feature data matrix according to the corresponding rules; Then, a tensor-based multi-feature affinity graph learning algorithm is proposed, in which tensor is introduced in the algorithm to effectively obtain the higher-order information of image data, and use the projection matrix to embed the original data in the low-dimensional space to decrease the dimension, while minimizing the residual error of each view feature and assign appropriate weights according to the importance of the feature information; finally, use spectral clustering performs clustering and segmentation on affinity graphs to obtain final clustering results and segmented images. In addition, an optimization iterative algorithm based on the alternating multiplier direction method is designed, which effectively solves the problem of solving the TRMFAL model. Sufficient experimental comparisons are carried out on multiple public datasets, and the results prove that the proposed method achieves the optimal clustering performance and segmentation effect. [ABSTRACT FROM AUTHOR]