The object detection model uses combined extraction with KNN and RF classification
<|reference_start|>The object detection model uses combined extraction with KNN and RF classification: Object detection plays an important role in various fields. Developing detection models for 2D objects that experience rotation and texture variations is a challenge. In this research, the initial stage of the proposed model integrates the gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP) texture feature extraction to obtain feature vectors. The next stage is classifying features using k-nearest neighbors (KNN) and random forest (RF), as well as voting ensemble (VE). System testing used a dataset of 4,437 2D images, the results for KNN accuracy were 92.7% and F1-score 92.5%, while RF performance was lower. Although GLCM features improve performance on both algorithms, KNN is more consistent. The VE approach provides the best performance with an accuracy of 93.9% and an F1 score of 93.8%, this shows the effectiveness of the ensemble technique in increasing object detection accuracy. This study contributes to the field of object detection with a new approach combining GLCM and LBP as feature vectors as well as VE for classification<|reference_end|>
@article{kurniati2024the,
    title={The object detection model uses combined extraction with KNN and RF
  classification},
    author={Florentina Tatrin Kurniati, Daniel HF Manongga, Irwan Sembiring,
  Sutarto Wijono, Roy Rudolf Huizen},
    journal={IJEECS, pp 436-445, Vol 35, No 1 July 2024;
  https://ijeecs.iaescore.com/index.php/IJEECS/article/view/35888},
    year={2024},
    doi={10.11591/ijeecs.v35.i1.pp436-445},
    archivePrefix={arXiv},
    eprint={2405.05551},
    primaryClass={cs.CV}
}