Howis the weather: Automatic inference from images

Abstract

Low-cost monitoring cameras/webcams provide unique visual information. To take advantage of the vast image dataset captured by a typical webcam, we consider the problem of retrieving weather information from a database of still images. The task is to automatically label all images with different weather conditions (e.g., sunny, cloudy, and overcast), using limited human assistance. To address the drawbacks in existing weather prediction algorithms, we first apply image segmentation to the raw images to avoid disturbance of the non-sky region. Then, we propose to use multiple kernel learning to gather and select an optimal subset of image features from a certain feature pool. To further increase the recognition performance, we adopt multi-pass active learning for selecting the training set. The experimental results show that our weather recognition system achieves high performance.

Image-based geographical location estimation using web cameras

“…They describe a general approach to separate the sky from the rest of the image by determining the edge of the sky region. The accumulative frame difference between an image and the successive image is used to extract the sky region in [14]. The sky is assumed to be at the top of image and the clouds are dynamic.…”

“…Address all correspondence to Edward J. Delp, ace@ecn.purdue.edu night images by using images and sonar sensors. A method to retrieve the weather information from a database of still images was presented in [14]. The sky region of image was detected by using the difference of pixel values from successive image frames, morphological operations were then used to obtain a sky region mask.…”

Active Discriminative Dictionary Learning for Weather Recognition

“…Unlike Chen et al [9] who directly eliminated the nonsky regions of images, we extract two features in the nonsky parts of images based on the physical characteristics, which also can be used as powerful features to distinguish the different weather conditions. The interaction of light with the atmosphere has been studied as atmospheric optics.…”

“…); thus its performance largely depended on the accuracies of these technologies. Chen et al [9] employed support vector machine (SVM) with the help of active learning to classify the 2 Mathematical Problems in Engineering weather conditions of images into sunny, cloudy, or overcast. Nevertheless, they only extracted the features from sky part of the images and the useful information in nonsky part of images is neglected.…”

A system for large-scale analysis of distributed cameras

“…Thakur et al [6] used webcams to study vehicular traffic and the mobility models. Chen et al [7] detected weather from images. Jacobs et al [8] developed analysis methods to separate the AMOS dataset into subsets based on the scene in the images, such as the weather or the terrain.…”