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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Optimizing multimodal scene recognition through relevant feature selection approach for scene classification.

Sumathi K1, Pramod Kumar S1, H R Mahadevaswamy2

  • 1JNN College of Engineering, Shimoga, Karnataka, India.

Methodsx
|March 10, 2025
PubMed
Summary

This study enhances scene classification using multimodal feature extraction and feature selection with transfer learning. The novel method improves efficiency and accuracy, offering a scalable solution for computer vision tasks.

Keywords:
Feature extractionMultimodal Feature extraction and Relevant Feature selection using Filter and Embedded approachMutual informationScene classification

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Deep learning models for scene classification are time-intensive to build from scratch.
  • Transfer learning offers an efficient alternative using predefined models.

Purpose of the Study:

  • To introduce a novel multimodal feature extraction and selection technique for efficient transfer learning in scene classification.
  • To enhance the performance and computational efficiency of scene classification models.

Main Methods:

  • Leveraging convolutional neural networks (CNNs) for multimodal feature extraction.
  • Applying a feature selection technique (MIFS-based approach) to improve model efficiency.
  • Executing the proposed method on the Scene (6 classes) and AID datasets.

Main Results:

  • The MIFS-based approach significantly reduces computational overhead.
  • Achieved competitive or superior classification accuracy compared to existing methods.
  • Demonstrated the scalability and effectiveness of the proposed methodology.

Conclusions:

  • The proposed method provides an efficient and effective solution for scene classification.
  • Offers potential for real-time recognition and automated systems in computer vision.