Two-class Weather Classification

Cewu Lu          Di Lin           Jiaya Jia          Chi-Keung Tang

Abstract

Given a single outdoor image this paper proposes a collaborative learning approach for labeling the image as either sunny or cloudy. Never adequately addressed, this two-class labeling problem is by no means trivial given the great variety of outdoor images. Our weather feature combines everyday weather cues after properly encoding them into feature vectors. These encoded cues 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. The other contribution consists of a new weather image dataset consisting of 10K sunny and cloudy images which is freely available with the executable of our implementation.

Downloads

"Two-class Weather Classification" Cewu Lu, Di Lin, Jiaya Jia, Chi-Keung Tang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014    [Paper (pdf, 4MB)]   [Matlab Executable (real-time classification)]   [Sky Detector]   [Weather Dataset (1.20 GB)]