A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN Scheme for Scene Classification using Object Detection Inference
<|reference_start|>A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN Scheme for Scene Classification using Object Detection Inference: Scene understanding plays an important role in several high-level computer vision applications, such as autonomous vehicles, intelligent video surveillance, or robotics. However, too few solutions have been proposed for indoor/outdoor scene classification to ensure scene context adaptability for computer vision frameworks. We propose the first Lightweight Hybrid Graph Convolutional Neural Network (LH-GCNN)-CNN framework as an add-on to object detection models. The proposed approach uses the output of the CNN object detection model to predict the observed scene type by generating a coherent GCNN representing the semantic and geometric content of the observed scene. This new method, applied to natural scenes, achieves an efficiency of over 90\% for scene classification in a COCO-derived dataset containing a large number of different scenes, while requiring fewer parameters than traditional CNN methods. For the benefit of the scientific community, we will make the source code publicly available: https://github.com/Aymanbegh/Hybrid-GCNN-CNN.<|reference_end|>
@article{beghdadi2024a,
    title={A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN
  Scheme for Scene Classification using Object Detection Inference},
    author={Ayman Beghdadi, Azeddine Beghdadi, Mohib Ullah, Faouzi Alaya Cheikh,
  Malik Mallem},
    journal={arXiv preprint arXiv:2407.14658},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.14658},
    primaryClass={cs.CV cs.AI}
}