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Discriminative Shape Feature Pooling in Deep Neural Networks.

Gang Hu¹, Chahna Dixit², Guanqiu Qi¹

¹Computer Information Systems Department, State University of New York at Buffalo State, Buffalo, NY 14222, USA.

Journal of Imaging

|May 27, 2022

Summary

This study introduces a novel method to enhance deep learning models by integrating handcrafted shape features. This approach improves image representation, performance, and reduces computational costs for both large and small datasets.

Keywords:

classification edge pooling shape feature

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Deep learning excels at generic feature extraction from large datasets.
Handcrafted features offer domain-specific knowledge and intuitive visual understanding.
Integrating handcrafted and deep features faces challenges with parameter quality.

Purpose of the Study:

To propose a method for enriching deep network features using discriminative shape information.
To guide neural network parameter updates with explicit domain knowledge.
To generate image representations benefiting from both handcrafted and deep learned features.

Main Methods:

Injecting discriminative shape features (edge tokens, curve partitioning points) into deep networks.
Adjusting the internal parameter update process of neural networks.
Training modified neural networks with domain knowledge guidance.

Main Results:

The proposed method effectively enriches deep network features.
Experimental results confirm efficacy on both large and small training datasets.
Improved performance and reduced computational costs compared to existing models.

Conclusions:

The method successfully integrates handcrafted and deep learned features.
The approach offers a robust solution for improving deep learning models in computer vision tasks.
This technique enhances model performance and efficiency across various dataset sizes.