Multi-class weather classification on single images

Multi-class weather classification from single images is a fundamental operation in many outdoor computer vision applications. However, it remains difficult and the limited work is carried out for addressing the difficulty. Moreover, existing method is based on the fixed scene. In this paper we present a method for any scenario multi-class weather classification based on multiple weather features and multiple kernel learning. Our approach extracts multiple weather features and takes properly processing. By combining these features into high dimensional vectors, we utilize multiple kernel learning to learn an adaptive classifier. We collect an outdoor image set that contains 20K images called MWI (Multi-class Weather Image) set. Experimental results show that the proposed method can efficiently recognize weather on MWI dataset.