Metric learning for weather image classification

Fang Ju Lin*, Tsaipei Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Image classification is a core task in many applications of computer vision. Recognition of weather conditions based on large-volume image datasets is a challenging problem. However, there has been little research on weather-related recognition using color images, particularly with large datasets. In this study, we proposed a metric learning framework to investigate a two-class weather classification problem. We improve the classification accuracy using metric learning approaches. Extracting features from images is a challenging task and practical requirements such as domain knowledge and human intervention. In this paper, we define several categories of weather feature cures based on observations of outdoor images captured under different weather conditions. Experimental results show that a classifier based on metric learning framework is effective in weather classification and outperforms the previous approach when using the same dataset.

Original languageEnglish
Pages (from-to)13309-13321
Number of pages13
JournalMultimedia Tools and Applications
Issue number11
StatePublished - 1 Jun 2018


  • Information-theoretic metric learning
  • k nearest neighbor
  • Large-margin nearest-neighbor metric learning
  • Metric learning
  • Weather features
  • Weather image classification

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