A New Lightweight Hybrid Graph Convolutional Neural Network -- CNN Scheme for Scene Classification using Object Detection Inference

TL;DR

This paper introduces a lightweight hybrid graph convolutional neural network framework that enhances scene classification accuracy and efficiency by leveraging object detection outputs, suitable for indoor and outdoor environments.

Contribution

It presents the first LH-GCNN-CNN framework that combines object detection with graph neural networks for improved scene classification in a lightweight manner.

Findings

Achieves over 90% accuracy on a large scene dataset

Requires fewer parameters than traditional CNN methods

Effective for natural scene classification

Abstract

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.