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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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Using Non-Additive Entropy to Enhance Convolutional Neural Features for Texture Recognition.

Joao Florindo1, Konradin Metze2

  • 1Institute of Mathematics, Statistics and Scientific Computing, University of Campinas, Campinas 13083-859, Brazil.

Entropy (Basel, Switzerland)
|October 23, 2021
PubMed
Summary

This study introduces non-additive entropy for improved convolutional neural network (CNN) texture description. This novel approach enhances texture recognition accuracy on benchmark datasets and plant species identification.

Keywords:
convolutional neural networksimage descriptorsnon-additive entropytexture recognition

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Analysis

Background:

  • Texture description is crucial for image analysis.
  • Convolutional Neural Networks (CNNs) excel at feature extraction but can be improved for texture tasks.
  • Existing texture analysis methods have limitations.

Purpose of the Study:

  • To enhance CNN performance for texture description using non-additive entropy.
  • To introduce a local transform for pixel-wise entropy measurement.
  • To evaluate the proposed method on benchmark datasets and a real-world application.

Main Methods:

  • A local transform was developed to associate each pixel with local entropy.
  • This entropy-based representation was used as input for a pretrained CNN.
  • Performance was evaluated on KTH-TIPS-2b, FMD databases, and Brazilian plant species identification.

Main Results:

  • The method achieved 84.4% accuracy on the KTH-TIPS-2b database and 77.7% on the FMD database.
  • An accuracy of 88.5% was obtained for Brazilian plant species identification.
  • The approach outperformed several state-of-the-art texture analysis methods.

Conclusions:

  • Non-additive entropy provides a powerful representation for texture analysis with CNNs.
  • The proposed method demonstrates significant potential for deep learning-based texture recognition.
  • This entropy-driven feature extraction shows promise for complex identification tasks.