Large scale performance measurement of content-based automated image-orientation detection

With the proliferation of digital cameras and self-publishing of photos, automatic detection of image orientation will become an important part of photo management systems. In this paper, we perform a large scale empirical test to determine whether the common techniques to automatically determine a photo's orientation are robust enough to handle the breadth of real-world images. We use a wide variety of features and color-spaces to address this problem. We use test photos gathered from the Web and photo collections, including photos that are in color and black and white, realistic and abstract, and outdoor and indoor. Results show that current methods give satisfactory results on only a small subset of these images.