Measure for the Difference Between LBP Features Extracted From Original and Resized Cloud Images With Varying Resolutions

Abstract

Currently, ground-based cloud images taken by using a whole-sky imager are especially popular in the field of meteorology because of their high resolution and accurate cloud information. Cloud images are natural texture images, and thus texture features based on local binary patterns (LBPs) are widely used to analyze texture images. However, the high-computation cost of extracting LBP features from high-resolution cloud texture images may make this technique unacceptable in practical image processing. A commonly adopted method is to resize the original image to an appropriate version with a decreased resolution. But this process will inevitably result in information loss. Accordingly, a measure based on the Kullback-Leibler (KL) divergence of the difference between LBP histogram features extracted from the original and resized images with varying resolutions is reported in this letter. Furthermore, a confidence interval technique is introduced to validate the significance of such difference. Experiments based on real ground-based cloud images show the measurement results of KL divergence in LBP features extracted from original and resized images. The experimental results indicate that images should be resized with caution when performing image processing.