Image orientation detection using LBP-based features and logistic regression

Many imaging applications require that images are correctly orientated with respect to their content. In this work we present an algorithm for the automatic detection of the image orientation that relies on the image content as described by Local Binary Patterns (LBP). The detection is efficiently performed by exploiting logistic regression. The proposed algorithm has been extensively evaluated on more than 100,000 images taken from the Scene UNderstanding (SUN) database. The results show that our algorithm outperformed similar approaches in the state of the art, and its accuracy is comparable with that of human observers in detecting the correct orientation of a wide range of image contents.