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[bibtex]@InProceedings{Shin_2022_CVPR, author = {Shin, Nyeong-Ho and Lee, Seon-Ho and Kim, Chang-Su}, title = {Moving Window Regression: A Novel Approach to Ordinal Regression}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {18760-18769} }
Moving Window Regression: A Novel Approach to Ordinal Regression
Abstract
A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank (rho-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors (rho-regressors) to predict rho-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 rho-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.
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