Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016, Article ID 8272859, 12 pages
Research Article

Active Discriminative Dictionary Learning for Weather Recognition

1School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China
2School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China
3Key Laboratory of Intelligent Information Processing of Jilin Universities, Northeast Normal University, Changchun 130117, China
4College of Statistics, Capital University of Economics and Business, Beijing 100070, China

Received 12 December 2015; Accepted 22 March 2016

Academic Editor: Xiao-Qiao He

Copyright © 2016 Caixia Zheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Weather recognition based on outdoor images is a brand-new and challenging subject, which is widely required in many fields. This paper presents a novel framework for recognizing different weather conditions. Compared with other algorithms, the proposed method possesses the following advantages. Firstly, our method extracts both visual appearance features of the sky region and physical characteristics features of the nonsky region in images. Thus, the extracted features are more comprehensive than some of the existing methods in which only the features of sky region are considered. Secondly, unlike other methods which used the traditional classifiers (e.g., SVM and K-NN), we use discriminative dictionary learning as the classification model for weather, which could address the limitations of previous works. Moreover, the active learning procedure is introduced into dictionary learning to avoid requiring a large number of labeled samples to train the classification model for achieving good performance of weather recognition. Experiments and comparisons are performed on two datasets to verify the effectiveness of the proposed method.