Automated Silhouette Extraction for Mountain Recognition

With the rise of digital photography and easy sharing of images over the internet, a huge amount of images with no notion of what they are showing exists. In order to overcome this problem, we – for the example of mountain recognition – introduce a method, that is able to automatically recognise a mountain shown in a photography. Our method does not require GPS information stored in the image, since most images are not GPS tagged, either because of the absence of a GPS sensor in the device or because it has been deactivated for a lesser power consumption, which often is the case with smartphones. Instead, we propose a method that is able to automatically extract the mountain’s silhouette from a given image. This silhouette is then cleaned by removing artefacts and outliers, such as trees and clouds, with a histogram based approach. Finally, the cleaned silhouette can be compared to reference data in order to recognise the mountain that is shown in the picture. For this, time series comparison techniques can be used to find matching silhouettes. However, because of the huge number of reference silhouettes to compare against, we argue, that a preselection of those silhouettes is necessary and point out approaches to this problem.