Inverse Renormalization Group Transformation in Bayesian Image Segmentations
This work addresses computational efficiency for image segmentation tasks, but it appears incremental as it builds on existing Bayesian approaches with a specific optimization.
The paper tackled the problem of high computational time in Bayesian image segmentation by proposing a new algorithm combining loopy belief propagation with an inverse real space renormalization group transformation, resulting in a reduction of computational time to less than one-tenth of that of conventional methods.
A new Bayesian image segmentation algorithm is proposed by combining a loopy belief propagation with an inverse real space renormalization group transformation to reduce the computational time. In results of our experiment, we observe that the proposed method can reduce the computational time to less than one-tenth of that taken by conventional Bayesian approaches.