Categorization of cloud image patches using an improved texton-based approach

We propose a modified texton-based classification approach that integrates both color and texture information for improved classification results. We test our proposed method for the task of cloud classification on SWIMCAT, a large new database of cloud images taken with a ground-based sky imager, with very good results. We perform an extensive evaluation, comparing different color components, filter banks, and other parameters to understand their effect on classification accuracy. Finally, we release the SWIMCAT dataset that was created for the task of cloud categorization.