RSCM: Region Selection and Concurrency Model for Multi-Class Weather Recognition.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Meteorology

Background:

• Weather condition recognition is crucial for various applications.
• Existing methods often overlook the importance of regional cues.
• Accurate weather classification requires robust feature representation.

Purpose of the Study:

• To emphasize the importance of regional cues in weather condition recognition.
• To address challenges in regional feature representation and differentiation.
• To develop a novel deep learning framework for improved weather classification.

Main Methods:

• Construction of a multi-class benchmark dataset with 65,000 images across six weather categories (sunny, cloudy, rainy, snowy, haze, thunder).
• Proposal of a deep learning framework named region selection and concurrency model (RSCM).
• Evaluation of RSCM on the newly constructed dataset and a public weather recognition dataset.

Main Results:

• The proposed benchmark dataset supports weather classification and attribute recognition.
• The region selection and concurrency model (RSCM) effectively discovers regional properties and concurrency.
• RSCM demonstrates strong performance in weather recognition tasks.

Conclusions:

• Regional cues are vital for accurate weather condition recognition.
• The developed RSCM framework offers a promising approach for weather classification.
• The new benchmark dataset facilitates further research in weather recognition.