GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning
<|reference_start|>GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning: In the era of modern technology, object detection using the Gray Level Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object recognition processes. It finds applications in real-time scenarios such as security surveillance and autonomous vehicle navigation, among others. Computational efficiency becomes a critical factor in achieving real-time object detection. Hence, there is a need for a detection model with low complexity and satisfactory accuracy. This research aims to enhance computational efficiency by selecting appropriate features within the GLCM framework. Two classification models, namely K-Nearest Neighbours (K-NN) and Support Vector Machine (SVM), were employed, with the results indicating that K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational complexity. Specifically, K-NN, when utilizing a combination of Correlation, Energy, and Homogeneity features, achieves a 100% accuracy rate with low complexity. Moreover, when using a combination of Energy and Homogeneity features, K-NN attains an almost perfect accuracy level of 99.9889%, while maintaining low complexity. On the other hand, despite SVM achieving 100% accuracy in certain feature combinations, its high or very high complexity can pose challenges, particularly in real-time applications. Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity. This research provides valuable insights for optimizing object detection in various applications requiring both high accuracy and rapid responsiveness.<|reference_end|>
@article{kurniati2024glcm-based,
    title={GLCM-Based Feature Combination for Extraction Model Optimization in
  Object Detection Using Machine Learning},
    author={Florentina Tatrin Kurniati, Daniel HF Manongga, Eko Sediyono, Sri
  Yulianto Joko Prasetyo, Roy Rudolf Huizen},
    journal={JITEKI,December 2023,
  http://journal.uad.ac.id/index.php/JITEKI/article/view/27842},
    year={2024},
    doi={10.26555/jiteki.v9i4.27842},
    number={Vol. 9, No. 4, pp. 1196-1205},
    archivePrefix={arXiv},
    eprint={2404.04578},
    primaryClass={cs.CV}
}