Convolutional Ordinal Regression Forest for Image Ordinal Estimation.

Area of Science:

• Computer Vision
• Machine Learning
• Artificial Intelligence

Background:

• Image ordinal estimation predicts ordered labels, often treated as ordinal regression (OR).
• Existing OR methods using sequential binary classifiers neglect inter-classifier relationships, risking global order preservation.
• This limitation impacts the accuracy and stability of image ordinal estimation tasks.

Purpose of the Study:

• To propose a novel Convolutional Ordinal Regression Forest (CORF) for precise and stable image ordinal estimation.
• To address the limitations of existing OR methods by ensuring global ordinal relationships are preserved.
• To integrate ordinal regression with differentiable decision trees and convolutional neural networks.

Main Methods:

• The proposed CORF approach integrates ordinal regression (OR) with differentiable decision trees within a convolutional neural network (CNN).
• It learns an ordinal distribution for OR by simultaneously optimizing multiple binary classifiers, unlike independent training.
• Differentiable decision trees are trained end-to-end with the ordinal distribution for integrated learning.

Main Results:

• The CORF method demonstrated significant improvements in accuracy and stability on image ordinal estimation tasks.
• Effectiveness was verified on facial age estimation and image esthetic assessment benchmarks.
• The approach outperformed existing state-of-the-art ordinal regression methods.

Conclusions:

• The proposed CORF method effectively preserves global ordinal relationships for image ordinal estimation.
• Simultaneous optimization of classifiers and end-to-end training of differentiable decision trees enhance precision and stability.
• CORF offers a superior alternative to current methods for tasks like facial age and esthetic assessment.