Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82887
Title: Multi-level semantic labeling of Sky/cloud images
Authors: Dev, Soumyabrata
Lee, Yee Hui
Winkler, Stefan
Keywords: Clustering
likelihood estimation
groundbased sky imaging
Issue Date: 2015
Source: Dev, S., Lee, Y. H., & Winkler, S. (2015). Multi-level semantic labeling of Sky/cloud images. 2015 IEEE International Conference on Image Processing (ICIP).
Conference: 2015 IEEE International Conference on Image Processing (ICIP)
Abstract: 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
URI: https://hdl.handle.net/10356/82887
http://hdl.handle.net/10220/40381
DOI: 10.1109/ICIP.2015.7350876
Schools: School of Electrical and Electronic Engineering 
Rights: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICIP.2015.7350876].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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