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Updated: Sep 12, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Published on: January 5, 2024

SFBM: Shared Feature Bias Mitigating for Long-Tailed Image Recognition.

Xinqiao Zhao, Mingjie Sun, Eng Gee Lim

IEEE Transactions on Neural Networks and Learning Systems

|August 8, 2025

Summary

This summary is machine-generated.

This study introduces a Shared Feature Bias Mitigating (SFBM) framework to improve recognition models dealing with imbalanced datasets. SFBM effectively reduces bias in neural networks, enhancing performance on underrepresented classes.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Real-world data often exhibits long-tailed distributions, where a few classes dominate while most have few samples.
This imbalance compromises the performance of standard recognition models, leading to misclassifications, especially for tail-class samples.
A key issue is shared feature bias in neural networks, where features common to multiple classes are incorrectly prioritized for head classes.

Purpose of the Study:

To address the performance degradation of recognition models caused by long-tailed distributions.
To propose a novel framework, Shared Feature Bias Mitigating (SFBM), to correct shared feature bias in neural network classifiers.
To enhance the accuracy of recognition models on tail-class data without increasing inference complexity.

Main Methods:

Developed the Shared Feature Bias Mitigating (SFBM) framework.
Introduced two parallel classifiers trained concurrently with a baseline classifier using a specialized training loss.
Estimated shared feature components in baseline classifier weights using parallel classifier weights.
Rectified the baseline classifier by removing estimated shared features and adding class-specific parallel classifier weights.

Main Results:

The SFBM framework demonstrated broad compatibility with various recognition methods.
SFBM maintained high computational efficiency, introducing no additional computation during inference.
Experiments on CIFAR10/100-LT, ImageNet-LT, and iNaturalist 2018 showed consistent performance boosts across state-of-the-art methods when SFBM was incorporated during training.

Conclusions:

The proposed SFBM framework effectively mitigates shared feature bias in neural network classifiers.
SFBM significantly improves the performance of recognition models on long-tailed datasets.
The framework offers a computationally efficient and broadly applicable solution for imbalanced learning scenarios.