Paper Info
Title
Convolutional Ordinal Regression Forest for Image Ordinal Estimation
Abstract
Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression (OR) problem. Recent methods formulate an OR 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 OR approach, termed convolutional OR forest (CORF), for image ordinal estimation, which can integrate OR 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">independently</i> , the proposed method aims at learning an ordinal distribution for OR by optimizing those binary classifiers <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">simultaneously</i> . 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 esthetic assessment, showing significant improvements and better stability over the state-of-the-art OR methods.
Year
DOI
Venue
2022
10.1109/TNNLS.2021.3055816
IEEE Transactions on Neural Networks and Learning Systems
Keywords
DocType
Volume
Face,Neural Networks, Computer,Regression Analysis
Journal
33
Issue
ISSN
Citations 
8
2162-237X
2
PageRank 
References 
Authors
0.37
26
8
Name
Order
Citations
PageRank
Haiping Zhu1332.48
Hongming Shan25512.35
Yuheng Zhang320.37
Lingfu Che420.37
Xiaoyang Xu520.37
Junping Zhang6117359.62
Jianbo Shi7102071031.66
Fei-Yue Wang85273480.21