Scene-free multi-class weather classification on single images.

Multi-class weather classification is a fundamental and significant technique which has many potential applications, such as video surveillance and intelligent transportation. However, it is a challenging task due to the diversity of weather and lack of discriminate feature. Most existing weather classification methods only consider two-class weather conditions such as sunny-rainy or sunny-cloudy weather. Moreover, they predominantly focus on a fixed scene such as popular tourism and traffic scenario. In this paper, we propose a novel method for scene-free multi-class weather classification from single images based on multiple category-specific dictionary learning and multiple kernel learning. To improve the discrimination of image representation and enhance the performance of multiple weather classification, our approach extracts multiple weather features and learns dictionaries based on these features. To select a good subset of features, we utilize multiple kernel learning algorithm to learn an optimal linear combination of feature kernels. In addition, to evaluate the proposed approach, we collect an outdoor image set that contains 20 K images, called MWI (Multi-class Weather Image) set. Experimental results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]