Moving Window Regression: A Novel Approach to Ordinal Regression
This provides a novel method for ordinal regression tasks like age estimation, though it appears incremental as it builds on existing regression frameworks.
The authors tackled ordinal regression by introducing moving window regression (MWR), which uses relative ranks and iterative refinement to achieve state-of-the-art performance on benchmark datasets for facial age estimation and historical color image classification.
A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank ($ρ$-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors ($ρ$-regressors) to predict $ρ$-ranks within entire and specific rank ranges, respectively. Third, we refine an initial rank estimate iteratively by selecting two reference instances to form a search window and then estimating the $ρ$-rank within the window. Extensive experiments results show that the proposed algorithm achieves the state-of-the-art performances on various benchmark datasets for facial age estimation and historical color image classification. The codes are available at https://github.com/nhshin-mcl/MWR.