Summary of Real-time Weather Image Classification with Svm, by Eden Ship et al.
Real-Time Weather Image Classification with SVM
by Eden Ship, Eitan Spivak, Shubham Agarwal, Raz Birman, Ofer Hadar
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method leverages Support Vector Machine (SVM) algorithm to classify weather conditions in images with an impressive 92.8% accuracy, outperforming traditional machine learning methods and comparable to deep learning approaches. The approach utilizes a robust set of features, including brightness, saturation, noise level, blur metric, edge strength, motion blur, Local Binary Patterns (LBP), edges, color histogram mean, and variance for blue, green, and red channels. The study highlights the effectiveness of texture, color, and edge-related features in capturing unique characteristics of different weather conditions. This research advances the state-of-the-art in weather image classification and provides insights into critical features necessary for accurate weather condition differentiation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Weather images can be classified into four categories: rainy, low light, haze, and clear. The goal is to improve object detection and classification models under varying weather conditions. A new method uses Support Vector Machine (SVM) algorithm and a set of features like brightness, saturation, and color histogram to correctly identify the weather. This approach achieved an accuracy of 92.8%, which is better than many other methods. It’s also fast and efficient, making it useful for applications like autonomous vehicles. |
Keywords
» Artificial intelligence » Classification » Deep learning » Image classification » Machine learning » Object detection » Support vector machine
