Rank Approximation of a Tensor with Applications in Color Image and Video Processing
arXiv:1904.12375
AI Analysis
For practitioners in image/video processing, it offers a new method for tensor rank estimation, though incremental as it combines existing techniques.
The paper proposes a block coordinate descent algorithm that uses ℓ1-norm sparse optimization to estimate tensor rank and compute canonical polyadic decomposition, applied to color images and single moving object videos.
We propose a block coordinate descent type algorithm for estimating the rank of a given tensor. In addition, the algorithm provides the canonical polyadic decomposition of a tensor. In order to estimate the tensor rank we use sparse optimization method using $\ell_1$ norm. The algorithm is implemented on single moving object videos and color images for approximating the rank.