Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers \emph{independently}, the proposed method aims at learning an ordinal distribution for ordinal regression by optimizing those binary classifiers \emph{simultaneously}. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e. facial age estimation and image aesthetic assessment, showing significant improvements and better stability over the state-of-the-art ordinal regression methods.

翻译：图像或表面估计是预测某一图像的正反向标志, 可以归类为正反向问题。 最近的方法将一个正反向问题作为一系列二进制分类问题。 由于不同的二进制分类者之间的关系被忽视, 这种方法无法确保保持全球正反向关系。 我们提议一种新型的正反向方法, 称为“ 革命半反向森林” 或“ CORF ”, 用于图像或非正反向估计。 其次, 拟议的CORF 中不同的决定树可以结合一个革命性神经网络, 以获得精确和稳定的全球或非正反向关系。 拟议的CORF 的优点是双重的。 首先, 与其学习一系列双进制分类者 \ emph{ 不独立地}, 拟议的方法的目的是通过优化这些二进制分类者 \ emph{ situally reformal ormal assessional a pressional- impressional orizational oral orizal imational orizational orizational ortial ortiew ortiewal ormaview.