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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Analysis

    Background:

    • Classifying outdoor image weather (sunny/cloudy) is challenging due to image variability and post-capture editing.
    • Existing methods may not fully capture the nuances of weather conditions from visual data alone.

    Purpose of the Study:

    • To develop a robust method for automatic sunny/cloudy image classification.
    • To improve classification accuracy by integrating diverse feature types within a unified framework.

    Main Methods:

    • A collaborative learning approach combining data-driven convolutional neural network (CNN) features with curated weather-specific features.
    • A unified optimization framework that adapts to the presence or absence of weather cues.
    • A novel data augmentation scheme to enrich training data and a latent Support Vector Machine (SVM) framework for intensity transfer insensitivity.

    Main Results:

    • The proposed method achieves improved accuracy, up to 7-8% higher than previous work and sole CNN classification.
    • The collaborative feature approach demonstrates superior performance in distinguishing between sunny and cloudy outdoor images.
    • The system is robust to variations in image capture and post-processing.

    Conclusions:

    • The integrated approach of CNN and weather-specific features offers a significant advancement in automated weather classification from single outdoor images.
    • The developed method provides a more accurate and reliable solution for image-based weather determination.
    • The publicly available dataset and classifier facilitate further research and application in this domain.