A comparative study of pre-trained models for image feature extraction in weather image classification using orange data mining
Year: 2024
Type: article
Abstract: This paper presents a detailed comparative analysis of pre-trained models for feature extraction in the domain of weather image classification. Utilizing the orange data mining toolkit, we investigated the effectiveness of six prominent pre-trained models-InceptionV3, SqueezeNet, VGG-16, VGG-19, painter, and DeepLoc-in accurately classifying weather phenomena images. Among these models, InceptionV3, in conjunction with neural networks, emerged as the most effective, achieving a classification accuracy (CA) of 96.1%. Painter and SqueezeNet also showed strong performance, with accuracies of 95.1% and 86.7%, respectively, although they were surpassed by InceptionV3. VGG-16 and VGG-19 provided moderate accuracy, while DeepLoc underperformed significantly with a maximum accuracy of 56%. Neural networks consistently outperformed other classifiers across all models. This study highlights the critical importance of selecting appropriate pre-trained models to enhance the accuracy and reliability of weather image classification systems.
Authors: Pafan Doungpaisan, Peerapol Khunarsa
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403939075