This research presents a novel approach called “Multiple View Spectral
Segmentation based on Tensor Singular Value Decomposition” for the segmentation of
multi-view data. The algorithm utilizes three-rank tensors and constructs a probability
transfer matrix for all view data. By exploiting the low-rank nature of tensors in the
lateral, longitudinal, and vertical directions, the proposed procedure characterizes the
tensor's low-rank properties in each dimension using a multi-rank approach based on
tensor singular value decomposition (Tensor-SVD). Tensor-SVD decomposition, being
based on tube convolution, enables the model to capture spatial correlations more
effectively compared to other tensor resolution techniques and procedures based on
two-dimensional structure relationships. Furthermore, the use of Fourier transformation
allows for efficient calculations, thereby improving computational efficiency.
Experimental results demonstrate that the proposed tensor resolution model based on
Tensor-SVD achieves improved segmentation performance for multiple-view data.
Keywords: Computational efficiency, Fourier transformation, Low-Rank characterization, Multiple view spectral segmentation, Multi-rank tensor, Probability transfer matrix, Spatial correlation, Tensor singular value decomposition.
