Automated image-orientation detection: a scalable boosting approach

With the proliferation of digital cameras and self-publishing of photos, automatic detection of image orientation has become an important part of photo-management systems. In this paper, we present a novel system, based on combining the outputs of hundreds of classifiers trained with AdaBoost, to determine the upright orientation of an image. We thoroughly test our system on photos gathered from professional and amateur photo collections that have been taken with a variety of cameras (digital, film, camera phones). The test images include photos that are in color and black and white, realistic and abstract, and outdoor and indoor. As this system is intended for mass consumer deployment, efficiency in use and accessibility is paramount. Results show that the presented method surpasses similar methods based on Support Vector Machines, in terms of both accuracy and feasibility of deployment.

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