WCATN: Unsupervised deep learning to classify weather conditions from outdoor images. (August 2022)

• Record Type: Journal Article
• Authors: Xie, Kezhen; Huang, Lei; Wei, Zhiqiang; Zhang, Wenfeng; Qin, Qibing
• Is Part Of: Engineering applications of artificial intelligence. Volume 113(2022)
• Publication Date: 2022-08
• Subjects: Weather classification -- Weather feature learning -- Weather clustering -- Fine-tuning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104928
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Weather classification from single images is significant for many outdoor computer vision applications, while it has not been thoroughly studied. Existing methods view it as a supervised task under the guidance of weather labels. Though these methods have made great success, they are not applicable to real-world applications because of their reliance on massive human-annotated weather images. In this paper, we take the initial attempt to treat weather classification as an unsupervised task, i.e. to classify weather conditions from unlabeled outdoor images. Specifically, we propose a three-step unsupervised approach to automatically group images into weather clusters. In the first step, i.e. weather feature learning, semantically meaningful weather features are learned by employing a self-supervised task. Furthermore, we also introduce online triplet mining into the self-supervised task to extract discriminative weather features. In the second step, i.e. weather clustering, we design a learnable clustering method via mining the nearest neighbors and enforcing consistency between each sample and its nearest neighbors. To alleviate the uncertainty of weather clustering due to noisy nearest neighbors, we propose a self-labeling method as the third step to employ the already well-classified samples for fine-tuning. Experimental results on two publicly available weather image datasets demonstrate that the proposed approach achieves promising performance.