GLCM-Based Feature Combination for Extraction Model Optimization in
  Object Detection Using Machine Learning
Florentina Tatrin Kurniati Daniel HF Manongga Eko Sediyono Sri Yulianto Joko Prasetyo Roy Rudolf Huizen
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.