Two-Class Weather Classification

• Published in: 2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 3718 - 3725
• Main Authors: Lu, Cewu, Lin, Di, Jia, Jiaya, Tang, Chi-Keung
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Classification; Climatology; Clouds; Computer vision; Construction; Feature; Histograms; Image color analysis; Labeling; Learning; Meteorology; Outdoor; Pattern recognition; Scene; Training; Vectors; Weather
• DOI: 10.1109/CVPR.2014.475
• ISSN: 1063-6919

Given a single outdoor image, this paper proposes a collaborative learning approach for labeling it as either sunny or cloudy. Never adequately addressed, this twoclass classification problem is by no means trivial given the great variety of outdoor images. Our weather feature combines special cues after properly encoding them into feature vectors. They then work collaboratively in synergy under a unified optimization framework that is aware of the presence (or absence) of a given weather cue during learning and classification. Extensive experiments and comparisons are performed to verify our method. We build a new weather image dataset consisting of 10K sunny and cloudy images, which is available online together with the executable.