GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning

• URL: http://arxiv.org/abs/2404.04578v1
• Date: Sat, 6 Apr 2024 10:16:33 GMT
• Title: GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning
• Authors: Florentina Tatrin Kurniati, Daniel HF Manongga, Eko Sediyono, Sri Yulianto Joko Prasetyo, Roy Rudolf Huizen,
• Abstract summary: 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. The results indicate that K-NN outperforms SVM in terms of computational complexity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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