Multi-level semantic labeling of Sky/cloud images

• Main Authors: Dev, Soumyabrata, Lee, Yee Hui, Winkler, Stefan
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2016
• Subjects: Clustering; likelihood estimation; groundbased sky imaging
• Online Access: https://hdl.handle.net/10356/82887; http://hdl.handle.net/10220/40381
• Institution: Nanyang Technological University

Sky/cloud images captured by ground-based Whole Sky Imagers (WSIs) are extensively used now-a-days for various applications. In this paper, we learn the semantics of sky/cloud images, which allows an automatic annotation of pixels with different class labels. We model the various labels/classes with a continuous-valued multi-variate distribution. Using a set of training images, the distributions for different labels are learnt, and subsequently used for labeling test images. We also present a method to determine the number of clusters. Our proposed approach is the first for multi-class sky-cloud image annotation and achieves very good results