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Fractal adaptive weight synthesized-local directional pattern-based image classification using enhanced tree seed

Annalakshmi Ganesan1, Sakthivel Murugan Santhanam2

  • 1Underwater Acoustic Research Lab, Department of Electronics and Communications Engineering, Sri Sivasubramaniya Nadar College of Engineering, Rajiv Gandhi Salai, Kalavakkam, Chennai, 603110, India. anumevlsi@gmail.com.

Environmental Science and Pollution Research International
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PubMed
Summary

A novel fractal adaptive weight synthesized-local directional pattern (FAWS-LDP) method combined with an enhanced tree seed algorithm-extreme learning machine (ETSA-ELM) effectively classifies coral species. This approach offers superior performance for marine ecosystem monitoring and conservation efforts.

Keywords:
Coral imagesExtreme learning machineFractal dimensionTree seed optimization

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Area of Science:

  • Marine Biology
  • Ecosystem Monitoring
  • Computer Vision

Background:

  • Coral reefs are vital marine ecosystems facing significant threats from climate change and human activities.
  • Accurate and automated classification of coral species is crucial for conservation and tracking vulnerable populations.
  • Existing coral image classification methods may struggle with accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate a novel, highly accurate method for automatic coral species classification.
  • To introduce a new feature descriptor, fractal adaptive weight synthesized-local directional pattern (FAWS-LDP).
  • To optimize the extreme learning machine (ELM) classifier using an enhanced tree seed algorithm (ETSA).

Main Methods:

  • Proposed a new feature descriptor: fractal adaptive weight synthesized-local directional pattern (FAWS-LDP).
  • Employed an enhanced tree seed algorithm (ETSA) to optimize extreme learning machine (ELM) classifier parameters.
  • Compared the ETSA-ELM model against other metaheuristic algorithms (GA, PSO, ABC) using standard performance metrics.

Main Results:

  • The proposed FAWS-LDP descriptor effectively combines fractal and local directional features.
  • The ETSA-ELM model demonstrated consistently superior classification accuracy, sensitivity, and specificity compared to existing methods.
  • Statistical analysis using the Friedman test confirmed the efficiency of the proposed feature descriptor.

Conclusions:

  • The FAWS-LDP combined with ETSA-ELM presents a robust and efficient solution for automated coral species classification.
  • This advanced technique aids in the crucial task of monitoring and conserving threatened coral reef ecosystems.
  • The study highlights the potential of integrating novel feature descriptors with optimized machine learning models for ecological applications.