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Related Experiment Video

Updated: Nov 27, 2025

Cooling Rate Dependent Ellipsometry Measurements to Determine the Dynamics of Thin Glassy Films

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Image Representation Method Based on Relative Layer Entropy for Insulator Recognition.

Zhenbing Zhao¹, Hongyu Qi¹, Xiaoqing Fan¹

¹School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

This study introduces a new method using relative layer entropy to find optimal layers in deep convolutional neural networks for image recognition. This approach improves feature representation and performance, especially for challenging datasets.

Keywords:

deep convolutional neural networks image representation insulator recognition relative layer entropy vector of locally aggregated descriptors

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Area of Science:

Computer Vision
Machine Learning
Deep Learning

Background:

Deep convolutional neural networks (DCNNs) are computationally intensive.
Selecting optimal feature representation layers in DCNNs remains a challenge.
Limited training data and object appearance types hinder recognition tasks.

Purpose of the Study:

To propose a novel image representation method based on relative layer entropy (IRM_RLE).
To identify the most suitable convolution layer for image recognition.
To enhance recognition performance with improved feature selection.

Main Methods:

Utilized an ImageNet pretrained DCNN to extract deep convolutional activations.
Introduced layer entropy and relative layer entropy to select optimal feature layers.
Employed VLAD coding for feature map vectorization and quantification.

Main Results:

The proposed IRM_RLE method achieves competitive performance across various datasets.
IRM_RLE outperforms state-of-the-art methods on indoor scenes and actions datasets.
The method effectively identifies crucial feature layers for image representation.

Conclusions:

IRM_RLE offers an effective strategy for selecting optimal feature layers in DCNNs.
The approach addresses computational complexity and improves recognition accuracy.
This method provides a valuable tool for feature representation in computer vision.