Learning to hash: forgiving hash functions and applications
Data Mining and Knowledge Discovery
What's up CAPTCHA?: a CAPTCHA based on image orientation
Proceedings of the 18th international conference on World wide web
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Hierarchical System for Content Based Categorization and Orientation of Consumer Images
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
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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.